In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous...In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous lung structures. Moreover, the nodules are of small size at their early stage of development. This poses a serious challenge to develop a Computer aided diagnosis (CAD) system with better false positive reduction. Hence, to reduce the false positives per scan and to deal with the challenges mentioned, this paper proposes a set of three diverse 3D Attention based CNN architectures (3D ACNN) whose predictions on given low dose Volumetric Computed Tomography (CT) scans are fused to achieve more effective and reliable results. Attention mechanism is employed to selectively concentrate/weigh more on nodule specific features and less weight age over other irrelevant features. By using this attention based mechanism in CNN unlike traditional methods there was a significant gain in the classification performance. Contextual dependencies are also taken into account by giving three patches of different sizes surrounding the nodule as input to the ACNN architectures. The system is trained and validated using a publicly available LUNA16 dataset in a 10 fold cross validation approach where a competition performance metric (CPM) score of 0.931 is achieved. The experimental results demonstrate that either a single patch or a single architecture in a one-to-one fashion that is adopted in earlier methods cannot achieve a better performance and signifies the necessity of fusing different multi patched architectures. Though the proposed system is mainly designed for pulmonary nodule detection it can be easily extended to classification tasks of any other 3D medical diagnostic computed tomography images where there is a huge variation and uncertainty in classification.展开更多
Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiop...Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance.However,challenges like delayed detection and disorganized communication persist.This work proposes a simple deep learning model for AFP surveillance,leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones.The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset.The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch,achieving superior accuracy,F1-score,precision,recall,and area under the receiver operating characteristic curve(AUC).It emerged as the optimal model,demonstrating the highest average AUC of 0.870±0.01.Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches(P<0.001).By bridging community reporting with health system response,this study offers a scalable solution for enhancing AFP surveillance in low-resource settings.The study is limited in terms of the quality of image data collected,necessitating future work on improving data quality.The establishment of a dedicated platform that facilitates data storage,analysis,and future learning can strengthen data quality.Nonetheless,this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images,with substantial implications for addressing global health challenges and disease eradication strategies.展开更多
文摘In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous lung structures. Moreover, the nodules are of small size at their early stage of development. This poses a serious challenge to develop a Computer aided diagnosis (CAD) system with better false positive reduction. Hence, to reduce the false positives per scan and to deal with the challenges mentioned, this paper proposes a set of three diverse 3D Attention based CNN architectures (3D ACNN) whose predictions on given low dose Volumetric Computed Tomography (CT) scans are fused to achieve more effective and reliable results. Attention mechanism is employed to selectively concentrate/weigh more on nodule specific features and less weight age over other irrelevant features. By using this attention based mechanism in CNN unlike traditional methods there was a significant gain in the classification performance. Contextual dependencies are also taken into account by giving three patches of different sizes surrounding the nodule as input to the ACNN architectures. The system is trained and validated using a publicly available LUNA16 dataset in a 10 fold cross validation approach where a competition performance metric (CPM) score of 0.931 is achieved. The experimental results demonstrate that either a single patch or a single architecture in a one-to-one fashion that is adopted in earlier methods cannot achieve a better performance and signifies the necessity of fusing different multi patched architectures. Though the proposed system is mainly designed for pulmonary nodule detection it can be easily extended to classification tasks of any other 3D medical diagnostic computed tomography images where there is a huge variation and uncertainty in classification.
基金supported by a fund from the International Development Research Centre(IDRC)(Grant No.109981e001)funded by Canada’s International Development Research Centre(IDRC)(Grant No.109981-001)+3 种基金support from IDRC and UK's Foreign,Commonwealth and Development Office(FCDO)(Grant No.110554-001)support from NSERC Discovery Grant(Grant No.RGPIN-2022-04559)NSERC Discovery Launch Supplement(Grant No:DGECR-2022-00454)New Frontier in Research Fund-Exploratory(Grant No.NFRFE-2021-00879).
文摘Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance.However,challenges like delayed detection and disorganized communication persist.This work proposes a simple deep learning model for AFP surveillance,leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones.The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset.The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch,achieving superior accuracy,F1-score,precision,recall,and area under the receiver operating characteristic curve(AUC).It emerged as the optimal model,demonstrating the highest average AUC of 0.870±0.01.Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches(P<0.001).By bridging community reporting with health system response,this study offers a scalable solution for enhancing AFP surveillance in low-resource settings.The study is limited in terms of the quality of image data collected,necessitating future work on improving data quality.The establishment of a dedicated platform that facilitates data storage,analysis,and future learning can strengthen data quality.Nonetheless,this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images,with substantial implications for addressing global health challenges and disease eradication strategies.
