An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect s...An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmenta...Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.展开更多
Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on opt...Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on optimizing machine learning(ML)algorithms,very limited attention has been paid to enhancing data quality for improved predictive performance.This study employs strategic data augmentation(DA)techniques to enhance the accuracy of shallow landslide prediction.Using five DA methods including singular spectrum analysis(SSA),moving averages(MA),wavelet denoising(WD),variational mode decomposition(VMD),and linear interpolation(LI),we utilize strategies such as smoothing,denoising,trend decomposition,and synthetic data generation to improve the training dataset.Four machine learning algorithms,i.e.artificial neural network(ANN),recurrent neural network(RNN),one-dimensional convolutional neural network(CNN1D),and long short-term memory(LSTM),are used to forecast landslide displacement.The case study of a landslide in southwest China shows the effectiveness of our approach in predicting landslide displacements,despite the inherent limitations of the monitoring dataset.VMD proves the most effective for smoothing and denoising,improving R^(2),RMSE,and MAPE by 172.16%,71.82%,and 98.9%,respectively.SSA addresses missing data,while LI is effective with limited data samples,improving metrics by 21.6%,52.59%,and 47.87%,respectively.This study demonstrates the potential of DA techniques to mitigate the impact of data defects on landslide prediction accuracy,with implications for similar cases.展开更多
Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research.Additionally,performing edge computing on lo...Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research.Additionally,performing edge computing on low-level devices using small neural networks can be an important research direction.In this paper,we use the EfficientNetV2B0 model for bird species classification,applying transfer learning on a dataset of 525 bird species.We also employ the BiRefNet model to remove backgrounds from images in the training set.The generated background-removed images are mixed with the original training set as a form of data augmentation.We aim for these background-removed images to help the model focus on key features,and by combining data augmentation with transfer learning,we trained a highly accurate and efficient bird species classification model.The training process is divided into a transfer learning stage and a fine-tuning stage.In the transfer learning stage,only the newly added custom layers are trained;while in the fine-tuning stage,all pre-trained layers except for the batch normalization layers are fine-tuned.According to the experimental results,the proposed model not only has an advantage in size compared to other models but also outperforms them in various metrics.The training results show that the proposed model achieved an accuracy of 99.54%and a precision of 99.62%,demonstrating that it achieves both lightweight design and high accuracy.To confirm the credibility of the results,we use heatmaps to interpret the model.The heatmaps show that our model can clearly highlight the image feature area.In addition,we also perform the 10-fold cross-validation on the model to verify its credibility.Finally,this paper proposes a model with low training cost and high accuracy,making it suitable for deployment on edge computing devices to provide lighter and more convenient services.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aim...Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aims to determine the composition of bioactive compounds in chikwangue, including biosurfactant-like molecules and proteins content. Antibacterial activities were investigated through the preliminary emulsification index of chikwangue and fermented paste. Antibacterial assay, 16S rRNA, cytK, hblD, nheB and entFM PCR amplifications, DNA sequence analysis, NCBI homology analysis, and phylogenic tree were performed using NGPhylogeny. fr and iTOL (interactive of live). Fermented cassava paste and chikwangue contain biosurfactants with an emulsification index of 50%. The total protein concentration in fermented cassava paste was 4 g/ml and the chikwangue was 2.5 g/mL Further sequence analysis showed that isolates shared a homology of up to 99.9% with Bacillus cereus PQ432941.1, B. licheniformis PQ432758.1, B. altitudinis PQ432754.1, B. subtilis PQ432759.1, B. mojavensis PQ432755.1, B. tequilensis MT994788.1, B. subtilis MT994789.1, Paenibacillus polymyxa PQ452544.1, B. velezensis PQ452545.1, B. thuringiensis PQ432763.1, B. pumilus PQ432762.1, B. subtilis MT994787.1, B. mycoides PQ432890.1, B. thuringiensis PQ432766.1, B. subtilis PQ432757.1 and B. amyloliquefaciens PQ432756.1. Importantly, the emulsification index (E24) ranged from 60 to 100% and the crude biosurfactant for the Bacillus strains mentioned above could easily inhibit the growth for pathogen Gram-negative bacteria (S. enterica, S. flexneri, E. coli, Klebsiella sp. and P. aeruginosa) with diameters ranging from 2.3 ± 0.1 cm to 5.5 ± 0.4 cm. On the other hand, the diameters of Gram-positive pathogenic bacteria (B. cereus and S. aureus) varied between 1.5 ± 0.5 cm and 4.0 ± 0.2 cm. These findings involve the promise purpose of Bacillus isolated from retted cassava, and this study systematically uncovered the biodiversity and distribution characteristics of retted paste cassava and chikwangue.展开更多
Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurat...Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurate classification.Enhancing the visibility of these elusive cell features helps train robust deep-learning models.However,the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community.To address this challenge,we introduce Salient Features Guided Augmentation(SFGA),an approach that strategically integrates machine learning and image processing.SFGA utilizes machine learning algorithms to identify crucial features within cell images,subsequently mapping these features to appropriate image processing techniques to enhance training images.By emphasizing salient features and aligning them with corresponding image processing methods,SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks.Our research undertakes a series of experiments,each exploring the performance of different datasets and data enhancement techniques in classifying cell types,highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics.Specifically,SFGA focuses on identifying tumor cells from tissue for extranodal extension detection,with the SFGA-enhanced dataset showing notable advantages in accuracy.We conducted a preliminary study of five experiments,among which the accuracy of the pleomorphism experiment improved significantly from 50.81%to 95.15%.The accuracy of the other four experiments also increased,with improvements ranging from 3 to 43 percentage points.Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis,contributing as a first step in using SFGA in medical image enhancement.展开更多
In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and th...In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.展开更多
The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to ...The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to be thoroughly investigated,primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear.This study developed a prediction model for abnormal TBM disc cutter wear,considering mixed ground conditions,by employing interpretable machine learning with data augmentation.An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions,and wear data was obtained from 65 cutterhead intervention(CHI)reports covering both mixed ground and hard rock sections.With a balanced training dataset obtained by data augmentation,an extreme gradient boosting(XGB)model delivered acceptable results with an accuracy of 0.94,an F1-score of 0.808,and a recall of 0.8.In addition,the accuracy for each individual disc cutter exhibited low variability.When employing data augmentation,a significant improvement in recall was observed compared to when it was not used,although the difference in accuracy and F1-score was marginal.The subsequent model interpretation revealed the chamber pressure,cutter installation radius,and torque as significant contributors.Specifically,a threshold in chamber pressure was observed,which could induce abnormal wear.The study also explored how elevated values of these influential contributors correlate with abnormal wear.The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters,enhancing the safety and efficiency of TBM operations.展开更多
With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown ma...With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.展开更多
Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(...Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.展开更多
Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that...Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that are not regularly exposed to the sun.Due to the striking similarities between benign and malignant lesions,skin cancer detection remains a problem,even for expert dermatologists.Considering the inability of dermatologists to di-agnose skin cancer accurately,a convolutional neural network(CNN)approach was used for skin cancer diag-nosis.However,the CNN model requires a significant number of image datasets for better performance;thus,image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model,because there are a limited number of medical images.This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection:(i)actinic keratoses,(ii)basal cell carcinoma,(iii)benign keratosis,(iv)dermatofibroma,(v)melanocytic nevi,(vi)melanoma,and(vii)vascular skin lesions.Five transfer learning models were used as the basis of the ensemble:MobileNet,EfficientNetV2B2,Xception,ResNeXt101,and Den-seNet201.In addition to the stratified 10-fold cross-validation,the results of each individual model were fused to achieve greater classification accuracy.An annealing learning rate scheduler and test time augmentation(TTA)were also used to increase the performance of the model during the training and testing stages.A total of 10,015 publicly available dermoscopy images from the HAM10000(Human Against Machine)dataset,which contained samples from the seven common skin lesion categories,were used to train and evaluate the models.The proposed technique attained 94.49%accuracy on the dataset.These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification.However,the weighted average of F1-score,recall,and precision were obtained to be 94.68%,94.49%,and 95.07%,respectively.展开更多
Breast augmentation with implants is a popular cosmetic surgery that enhances breast volume and contour through various placement planes.In this review,we examine the impact of subglandular,subpectoral,and subfascial ...Breast augmentation with implants is a popular cosmetic surgery that enhances breast volume and contour through various placement planes.In this review,we examine the impact of subglandular,subpectoral,and subfascial implant planes on postoperative outcomes and complication rates.Subglandular placement offers simplicity but is associated with higher risks of capsular contracture,hematoma,and rippling in patients with low tissue coverage.The subpectoral plane,widely adopted for its natural appearance and reduced capsular contracture risk,may cause dynamic deformity due to muscle contraction.Although technically challenging,the subfascial plane combines the benefits of soft tissue support and reduced implant displacement.We highlight the importance of choosing an optimal implant plane tailored to each patient’s anatomical and aesthetic needs to enhance surgical outcomes and minimize complications.Further research is needed to validate long-term efficacy,particularly for subfascial placement.展开更多
Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of ...Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of which are resistant to medical treatment.This study investigates the effect and outcome of magnetic sphincter augmentation(MSA),a minimally invasive treatment for GERD,in this population.Methods:A thorough search of the PubMed,Cochrane,Scopus,Web of Science,and Google Scholar databases from inception until June 6,2024 was performed to retrieve relevant studies that evaluated the effects of MSA on the GERD health-related quality of life(GERD-HRQL)score and the reduction in proton pump inhibitor(PPI)use in patients who underwent bariatric surgery.The“meta”package in RStudio version 2023.12.0 t 369 was used.Results:A total of eight studies were included in the systematic review and seven studies were included in the meta-analysis.MSA significantly reduced the GERD-HRQL score(MD?27.55[95%CI:30.99 to24.11],p<0.01)and PPI use(RR?0.23[95%CI:0.16 to 0.33],p<0.01).Conclusion:MSA is a viable treatment option for patients with GERD symptoms who undergo bariatric surgery.This approach showed promising results in terms of reducing the GERD-HRQL score and reducing the use of PPI.展开更多
Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for...Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks.Currently,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain identification.To tackle this challenge,we propose a novel method,SpaGRA(https://github.com/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph augmentation.SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning.Additionally,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random selection.Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols.Using SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data.Overall,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.展开更多
Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in ord...Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.展开更多
The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the p...The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the popular optimal function coefficient(OFC),sphere cap harmonic analysis(SCHA),kriging and inverse distance weighting(IDW)interpolation in ZTD spatial prediction and Beidou satellite navigation system(BDS)-PPP augmentation over China.For ZTD spatial prediction,the average time consumption of the OFC,kriging,and IDW methods is less than 0.1 s,which is significantly better than that of the SCHA method(63.157 s).The overall ZTD precision of the OFC is 3.44 cm,which outperforms those of the SCHA(9.65 cm),Kriging(10.6 cm),and IDW(11.8 cm)methods.We confirmed that the low performance of kriging and IDW is caused by their weakness in modelling ZTD variation in the vertical direction.To mitigate such deficiencies,an elevation normalization factor(ENF)is introduced into the kriging and IDW models(kriging-ENF and IDW-ENF).The overall ZTD spatial prediction accuracies of IDW-ENF and kriging-ENF are 2.80 cm and 2.01 cm,respectively,which are both superior to those of the OFC and the widely used empirical model GPT3(4.92 cm).For BDS-PPP enhancement,the ZTD provided by the kriging-ENF,IDW-ENF and OFC as prior constraints can effectively reduce the convergence time.Compared with unconstrained BDS-PPP,our proposed kriging-ENF outperforms IDW-ENF and OFC by reducing the horizontal and vertical convergence times by approximately 13.2%and 5.8%in Ningxia and 30.4%and 7.84%in Guangdong,respectively.These results indicate that kriging-ENF is a promising method for ZTD spatial prediction and BDS-PPP enhancement over China.展开更多
Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Aug...Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.展开更多
Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary w...Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.展开更多
基金supported by the Jiangsu Province IUR Cooperation Project (No.BY2021258)the Wuxi Science and Technology Development Fund Project (No.G20212028)。
文摘An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
基金supported by National Natural Science Foundation of China(61673186 and 61871196)Beijing Normal University Education Reform Project(jx2024040)Guangdong Undergraduate Universities Teaching Quality and Reform Project(jx2024309).
文摘Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.
基金supported by the National Natural Science Foundation of China(Grant No.42101089)Sichuan Science and Technology Program(2022YFS0586)the Open Fund of Key Laboratory of Mountain Hazards and Earth Surface Processes Chinese Academy of Sciences.
文摘Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on optimizing machine learning(ML)algorithms,very limited attention has been paid to enhancing data quality for improved predictive performance.This study employs strategic data augmentation(DA)techniques to enhance the accuracy of shallow landslide prediction.Using five DA methods including singular spectrum analysis(SSA),moving averages(MA),wavelet denoising(WD),variational mode decomposition(VMD),and linear interpolation(LI),we utilize strategies such as smoothing,denoising,trend decomposition,and synthetic data generation to improve the training dataset.Four machine learning algorithms,i.e.artificial neural network(ANN),recurrent neural network(RNN),one-dimensional convolutional neural network(CNN1D),and long short-term memory(LSTM),are used to forecast landslide displacement.The case study of a landslide in southwest China shows the effectiveness of our approach in predicting landslide displacements,despite the inherent limitations of the monitoring dataset.VMD proves the most effective for smoothing and denoising,improving R^(2),RMSE,and MAPE by 172.16%,71.82%,and 98.9%,respectively.SSA addresses missing data,while LI is effective with limited data samples,improving metrics by 21.6%,52.59%,and 47.87%,respectively.This study demonstrates the potential of DA techniques to mitigate the impact of data defects on landslide prediction accuracy,with implications for similar cases.
文摘Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research.Additionally,performing edge computing on low-level devices using small neural networks can be an important research direction.In this paper,we use the EfficientNetV2B0 model for bird species classification,applying transfer learning on a dataset of 525 bird species.We also employ the BiRefNet model to remove backgrounds from images in the training set.The generated background-removed images are mixed with the original training set as a form of data augmentation.We aim for these background-removed images to help the model focus on key features,and by combining data augmentation with transfer learning,we trained a highly accurate and efficient bird species classification model.The training process is divided into a transfer learning stage and a fine-tuning stage.In the transfer learning stage,only the newly added custom layers are trained;while in the fine-tuning stage,all pre-trained layers except for the batch normalization layers are fine-tuned.According to the experimental results,the proposed model not only has an advantage in size compared to other models but also outperforms them in various metrics.The training results show that the proposed model achieved an accuracy of 99.54%and a precision of 99.62%,demonstrating that it achieves both lightweight design and high accuracy.To confirm the credibility of the results,we use heatmaps to interpret the model.The heatmaps show that our model can clearly highlight the image feature area.In addition,we also perform the 10-fold cross-validation on the model to verify its credibility.Finally,this paper proposes a model with low training cost and high accuracy,making it suitable for deployment on edge computing devices to provide lighter and more convenient services.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
文摘Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aims to determine the composition of bioactive compounds in chikwangue, including biosurfactant-like molecules and proteins content. Antibacterial activities were investigated through the preliminary emulsification index of chikwangue and fermented paste. Antibacterial assay, 16S rRNA, cytK, hblD, nheB and entFM PCR amplifications, DNA sequence analysis, NCBI homology analysis, and phylogenic tree were performed using NGPhylogeny. fr and iTOL (interactive of live). Fermented cassava paste and chikwangue contain biosurfactants with an emulsification index of 50%. The total protein concentration in fermented cassava paste was 4 g/ml and the chikwangue was 2.5 g/mL Further sequence analysis showed that isolates shared a homology of up to 99.9% with Bacillus cereus PQ432941.1, B. licheniformis PQ432758.1, B. altitudinis PQ432754.1, B. subtilis PQ432759.1, B. mojavensis PQ432755.1, B. tequilensis MT994788.1, B. subtilis MT994789.1, Paenibacillus polymyxa PQ452544.1, B. velezensis PQ452545.1, B. thuringiensis PQ432763.1, B. pumilus PQ432762.1, B. subtilis MT994787.1, B. mycoides PQ432890.1, B. thuringiensis PQ432766.1, B. subtilis PQ432757.1 and B. amyloliquefaciens PQ432756.1. Importantly, the emulsification index (E24) ranged from 60 to 100% and the crude biosurfactant for the Bacillus strains mentioned above could easily inhibit the growth for pathogen Gram-negative bacteria (S. enterica, S. flexneri, E. coli, Klebsiella sp. and P. aeruginosa) with diameters ranging from 2.3 ± 0.1 cm to 5.5 ± 0.4 cm. On the other hand, the diameters of Gram-positive pathogenic bacteria (B. cereus and S. aureus) varied between 1.5 ± 0.5 cm and 4.0 ± 0.2 cm. These findings involve the promise purpose of Bacillus isolated from retted cassava, and this study systematically uncovered the biodiversity and distribution characteristics of retted paste cassava and chikwangue.
基金supported by grants fromthe North China University of Technology Research Start-Up Fund(11005136024XN147-14)and(110051360024XN151-97)Guangzhou Development Zone Science and Technology Project(2023GH02)+4 种基金the National Key R&D Program of China(2021YFE0201100 and 2022YFA1103401 to Juntao Gao)National Natural Science Foundation of China(981890991 to Juntao Gao)Beijing Municipal Natural Science Foundation(Z200021 to Juntao Gao)CAS Interdisciplinary Innovation Team(JCTD-2020-04 to Juntao Gao)0032/2022/A,by Macao FDCT,and MYRG2022-00271-FST.
文摘Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurate classification.Enhancing the visibility of these elusive cell features helps train robust deep-learning models.However,the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community.To address this challenge,we introduce Salient Features Guided Augmentation(SFGA),an approach that strategically integrates machine learning and image processing.SFGA utilizes machine learning algorithms to identify crucial features within cell images,subsequently mapping these features to appropriate image processing techniques to enhance training images.By emphasizing salient features and aligning them with corresponding image processing methods,SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks.Our research undertakes a series of experiments,each exploring the performance of different datasets and data enhancement techniques in classifying cell types,highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics.Specifically,SFGA focuses on identifying tumor cells from tissue for extranodal extension detection,with the SFGA-enhanced dataset showing notable advantages in accuracy.We conducted a preliminary study of five experiments,among which the accuracy of the pleomorphism experiment improved significantly from 50.81%to 95.15%.The accuracy of the other four experiments also increased,with improvements ranging from 3 to 43 percentage points.Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis,contributing as a first step in using SFGA in medical image enhancement.
基金funded by the Bavarian State Ministry of Science,Research and Art(Grant number:H.2-F1116.WE/52/2)。
文摘In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.
基金support of the“National R&D Project for Smart Construction Technology (Grant No.RS-2020-KA157074)”funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land,Infrastructure and Transport,and managed by the Korea Expressway Corporation.
文摘The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to be thoroughly investigated,primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear.This study developed a prediction model for abnormal TBM disc cutter wear,considering mixed ground conditions,by employing interpretable machine learning with data augmentation.An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions,and wear data was obtained from 65 cutterhead intervention(CHI)reports covering both mixed ground and hard rock sections.With a balanced training dataset obtained by data augmentation,an extreme gradient boosting(XGB)model delivered acceptable results with an accuracy of 0.94,an F1-score of 0.808,and a recall of 0.8.In addition,the accuracy for each individual disc cutter exhibited low variability.When employing data augmentation,a significant improvement in recall was observed compared to when it was not used,although the difference in accuracy and F1-score was marginal.The subsequent model interpretation revealed the chamber pressure,cutter installation radius,and torque as significant contributors.Specifically,a threshold in chamber pressure was observed,which could induce abnormal wear.The study also explored how elevated values of these influential contributors correlate with abnormal wear.The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters,enhancing the safety and efficiency of TBM operations.
基金supported in part by the National Key Research and Development Program of China(No.2022YFB4500800)the National Science Foundation of China(No.42071431).
文摘With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.
基金supported by the National Natural Science Foundation of China(Nos.81974355 and 82172524)Key Research and Development Program of Hubei Province(No.2021BEA161)+2 种基金National Innovation Platform Development Program(No.2020021105012440)Open Project Funding of the Hubei Key Laboratory of Big Data Intelligent Analysis and Application,Hubei University(No.2024BDIAA03)Free Innovation Preliminary Research Fund of Wuhan Union Hospital(No.2024XHYN047).
文摘Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.
文摘Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that are not regularly exposed to the sun.Due to the striking similarities between benign and malignant lesions,skin cancer detection remains a problem,even for expert dermatologists.Considering the inability of dermatologists to di-agnose skin cancer accurately,a convolutional neural network(CNN)approach was used for skin cancer diag-nosis.However,the CNN model requires a significant number of image datasets for better performance;thus,image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model,because there are a limited number of medical images.This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection:(i)actinic keratoses,(ii)basal cell carcinoma,(iii)benign keratosis,(iv)dermatofibroma,(v)melanocytic nevi,(vi)melanoma,and(vii)vascular skin lesions.Five transfer learning models were used as the basis of the ensemble:MobileNet,EfficientNetV2B2,Xception,ResNeXt101,and Den-seNet201.In addition to the stratified 10-fold cross-validation,the results of each individual model were fused to achieve greater classification accuracy.An annealing learning rate scheduler and test time augmentation(TTA)were also used to increase the performance of the model during the training and testing stages.A total of 10,015 publicly available dermoscopy images from the HAM10000(Human Against Machine)dataset,which contained samples from the seven common skin lesion categories,were used to train and evaluate the models.The proposed technique attained 94.49%accuracy on the dataset.These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification.However,the weighted average of F1-score,recall,and precision were obtained to be 94.68%,94.49%,and 95.07%,respectively.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(grant no.LQ22H150005).
文摘Breast augmentation with implants is a popular cosmetic surgery that enhances breast volume and contour through various placement planes.In this review,we examine the impact of subglandular,subpectoral,and subfascial implant planes on postoperative outcomes and complication rates.Subglandular placement offers simplicity but is associated with higher risks of capsular contracture,hematoma,and rippling in patients with low tissue coverage.The subpectoral plane,widely adopted for its natural appearance and reduced capsular contracture risk,may cause dynamic deformity due to muscle contraction.Although technically challenging,the subfascial plane combines the benefits of soft tissue support and reduced implant displacement.We highlight the importance of choosing an optimal implant plane tailored to each patient’s anatomical and aesthetic needs to enhance surgical outcomes and minimize complications.Further research is needed to validate long-term efficacy,particularly for subfascial placement.
文摘Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of which are resistant to medical treatment.This study investigates the effect and outcome of magnetic sphincter augmentation(MSA),a minimally invasive treatment for GERD,in this population.Methods:A thorough search of the PubMed,Cochrane,Scopus,Web of Science,and Google Scholar databases from inception until June 6,2024 was performed to retrieve relevant studies that evaluated the effects of MSA on the GERD health-related quality of life(GERD-HRQL)score and the reduction in proton pump inhibitor(PPI)use in patients who underwent bariatric surgery.The“meta”package in RStudio version 2023.12.0 t 369 was used.Results:A total of eight studies were included in the systematic review and seven studies were included in the meta-analysis.MSA significantly reduced the GERD-HRQL score(MD?27.55[95%CI:30.99 to24.11],p<0.01)and PPI use(RR?0.23[95%CI:0.16 to 0.33],p<0.01).Conclusion:MSA is a viable treatment option for patients with GERD symptoms who undergo bariatric surgery.This approach showed promising results in terms of reducing the GERD-HRQL score and reducing the use of PPI.
基金supported by the National Natural Science Foundation of China(Nos.62303271,U1806202,62103397)the Natural Science Foundation of Shandong Province(ZR2023QF081)Funding for open access charge:the National Natural Science Foundation of China(Nos.62303271,U1806202).
文摘Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks.Currently,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain identification.To tackle this challenge,we propose a novel method,SpaGRA(https://github.com/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph augmentation.SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning.Additionally,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random selection.Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols.Using SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data.Overall,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.
基金Natural Science Foundation of Zhejiang Province,Grant/Award Number:LY23F020025Science and Technology Commissioner Program of Huzhou,Grant/Award Number:2023GZ42Sichuan Provincial Science and Technology Support Program,Grant/Award Numbers:2023ZHCG0005,2023ZHCG0008。
文摘Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.
基金co-supported by the National Nature Science Foundation of China(No.12303071)the Shanghai Science and Technology Plan Project,China(No.23YF1455500)+1 种基金the China Postdoctoral Science Foundation(No.2023M743653)Ministry of Industry and Information Technology of China through the High Precision Timing Service Project(No.TC220A04A-80)。
文摘The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the popular optimal function coefficient(OFC),sphere cap harmonic analysis(SCHA),kriging and inverse distance weighting(IDW)interpolation in ZTD spatial prediction and Beidou satellite navigation system(BDS)-PPP augmentation over China.For ZTD spatial prediction,the average time consumption of the OFC,kriging,and IDW methods is less than 0.1 s,which is significantly better than that of the SCHA method(63.157 s).The overall ZTD precision of the OFC is 3.44 cm,which outperforms those of the SCHA(9.65 cm),Kriging(10.6 cm),and IDW(11.8 cm)methods.We confirmed that the low performance of kriging and IDW is caused by their weakness in modelling ZTD variation in the vertical direction.To mitigate such deficiencies,an elevation normalization factor(ENF)is introduced into the kriging and IDW models(kriging-ENF and IDW-ENF).The overall ZTD spatial prediction accuracies of IDW-ENF and kriging-ENF are 2.80 cm and 2.01 cm,respectively,which are both superior to those of the OFC and the widely used empirical model GPT3(4.92 cm).For BDS-PPP enhancement,the ZTD provided by the kriging-ENF,IDW-ENF and OFC as prior constraints can effectively reduce the convergence time.Compared with unconstrained BDS-PPP,our proposed kriging-ENF outperforms IDW-ENF and OFC by reducing the horizontal and vertical convergence times by approximately 13.2%and 5.8%in Ningxia and 30.4%and 7.84%in Guangdong,respectively.These results indicate that kriging-ENF is a promising method for ZTD spatial prediction and BDS-PPP enhancement over China.
文摘Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.
基金Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government(Grant No.20214000000140,Graduate School of Convergence for Clean Energy Integrated Power Generation)Korea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Ministry of Education(2021R1A6C101A449)the National Research Foundation of Korea grant funded by the Ministry of Science and ICT(2021R1A2C1095139),Republic of Korea。
文摘Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.
