{"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/long-tailed-classification-of-thorax-diseases","title":"Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study","arxiv_id":"2208.13365","date":"2022-08-29","proceeding":null,"authors":["Gregory Holste","Song Wang","Ziyu Jiang","Thomas C. Shen","George Shih","Ronald M. Summers","Yifan Peng","Zhangyang Wang"],"abstract":"Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual presentation of rare conditions by studying a few representative examples, teaching a machine to learn from such a \"long-tailed\" distribution is much more difficult, as standard methods would be easily biased toward the most frequent classes. In this paper, we present a comprehensive benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common \"head\" classes, but also the rare yet critical \"tail\" classes. To accomplish this, we introduce a challenging new long-tailed chest X-ray benchmark to facilitate research on developing long-tailed learning methods for medical image classification. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images. We evaluate both standard and state-of-the-art long-tailed learning methods on this new benchmark, analyzing which aspects of these methods are most beneficial for long-tailed medical image classification and summarizing insights for future algorithm design. The datasets, trained models, and code are available at https://github.com/VITA-Group/LongTailCXR.","url_abs":"https://arxiv.org/abs/2208.13365v1","url_pdf":"https://arxiv.org/pdf/2208.13365v1.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":"long-tailed-classification-of-thorax-diseases","repo_url":"https://github.com/vita-group/longtailcxr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"long-tail-learning","task_name":"Long-tail Learning"},{"task_slug":"medical-image-classification","task_name":"Medical Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[{"slug":"mimic-cxr-lt","name":"MIMIC-CXR-LT","full_name":"long-tailed version of MIMIC-CXR"},{"slug":"nih-cxr-lt","name":"NIH-CXR-LT","full_name":"Long-tailed (LT) NIH ChestXRay14"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/long-tail-learning-on-mimic-cxr-lt","task":"Long-tail Learning","dataset":"MIMIC-CXR-LT","model":"Decoupling (cRT)","rank_in_archive_order":1,"of":15,"metrics":{"Balanced Accuracy":"0.296"},"uses_additional_data":false},{"leaderboard":"/sota/long-tail-learning-on-mimic-cxr-lt","task":"Long-tail Learning","dataset":"MIMIC-CXR-LT","model":"Reweighted LDAM-DRW","rank_in_archive_order":2,"of":15,"metrics":{"Balanced Accuracy":"0.275"},"uses_additional_data":false},{"leaderboard":"/sota/long-tail-learning-on-mimic-cxr-lt","task":"Long-tail Learning","dataset":"MIMIC-CXR-LT","model":"Class-balanced 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