Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional a...Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.展开更多
Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has...Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.展开更多
With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is important.The routine real-time reverse transcription-polymerase ch...With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is important.The routine real-time reverse transcription-polymerase chain reaction(rRT-PCR)test that is currently used does not provide such high accuracy or speed in the screening process.Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques.In this study,a new convolutional neural network(CNN)framework for COVID-19 detection using computed tomography(CT)images is proposed.The EfficientNet architecture is applied as the backbone structure of the proposed network,in which feature maps with different scales are extracted from the input CT scan images.In addition,atrous convolution at different rates is applied to these multi-scale feature maps to generate denser features,which facilitates in obtaining COVID-19 findings in CT scan images.The proposed framework is also evaluated in this study using a public CT dataset containing 2482 CT scan images from patients of both classes(i.e.,COVID-19 and non-COVID-19).To augment the dataset using additional training examples,adversarial examples generation is performed.The proposed system validates its superiority over the state-of-the-art methods with values exceeding 99.10%in terms of several metrics,such as accuracy,precision,recall,and F1.The proposed system also exhibits good robustness,when it is trained using a small portion of data(20%),with an accuracy of 96.16%.展开更多
基金Open Access funding provided by the National Institutes of Health(NIH)The funding for this project was provided by NCATS Intramural Fund.
文摘Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.
基金supported by the National Key R&D Program of China(Grant No.2020YFB1709604)the State Key Laboratory of Mechanical System and Vibration(Grant No.MSVZD202103)+1 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102)。
文摘Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.
基金support provided from the Deanship of Scientific Research at King Saud University through the,Research Group No.(RG-1435-050.)。
文摘With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is important.The routine real-time reverse transcription-polymerase chain reaction(rRT-PCR)test that is currently used does not provide such high accuracy or speed in the screening process.Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques.In this study,a new convolutional neural network(CNN)framework for COVID-19 detection using computed tomography(CT)images is proposed.The EfficientNet architecture is applied as the backbone structure of the proposed network,in which feature maps with different scales are extracted from the input CT scan images.In addition,atrous convolution at different rates is applied to these multi-scale feature maps to generate denser features,which facilitates in obtaining COVID-19 findings in CT scan images.The proposed framework is also evaluated in this study using a public CT dataset containing 2482 CT scan images from patients of both classes(i.e.,COVID-19 and non-COVID-19).To augment the dataset using additional training examples,adversarial examples generation is performed.The proposed system validates its superiority over the state-of-the-art methods with values exceeding 99.10%in terms of several metrics,such as accuracy,precision,recall,and F1.The proposed system also exhibits good robustness,when it is trained using a small portion of data(20%),with an accuracy of 96.16%.
