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- W2810759071 abstract "We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medical imaging data with region-level annotations, our model was merely trained on imaging data with image-level labels to classify diseases, and is able to identify abnormal image regions simultaneously. Our model consists of a customized pooling structure and an adaptive DenseNet front-end, which can effectively recognize possible disease features for classification and localization tasks. Our method has been validated on the publicly available ChestX-ray14 dataset. Experimental results have demonstrated that our classification and localization prediction performance achieved significant improvement over the previous models on the ChestX-ray14 dataset. In summary, our network can produce accurate disease classification and localization, which can potentially support clinical decisions." @default.
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- W2810759071 date "2018-07-03" @default.
- W2810759071 modified "2023-09-27" @default.
- W2810759071 title "A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities." @default.
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