{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-weakly-supervised-adaptive-densenet-for","title":"A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities","arxiv_id":"1807.01257","date":"2018-07-03","proceeding":null,"authors":["Bo Zhou","Yuemeng Li","Jiangcong Wang"],"abstract":"We present a weakly supervised deep learning model for classifying thoracic\ndiseases and identifying abnormalities in chest radiography. In this work,\ninstead of learning from medical imaging data with region-level annotations,\nour model was merely trained on imaging data with image-level labels to\nclassify diseases, and is able to identify abnormal image regions\nsimultaneously. Our model consists of a customized pooling structure and an\nadaptive DenseNet front-end, which can effectively recognize possible disease\nfeatures for classification and localization tasks. Our method has been\nvalidated on the publicly available ChestX-ray14 dataset. Experimental results\nhave demonstrated that our classification and localization prediction\nperformance achieved significant improvement over the previous models on the\nChestX-ray14 dataset. In summary, our network can produce accurate disease\nclassification and localization, which can potentially support clinical\ndecisions.","url_abs":"http://arxiv.org/abs/1807.01257v2","url_pdf":"http://arxiv.org/pdf/1807.01257v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-weakly-supervised-adaptive-densenet-for","repo_url":"https://github.com/bbbbbbzhou/Weakly-Supervised-ChestXray-AdaptiveDenseNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.01257","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}