{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Holste G"],"funding":["NLM NIH HHS"],"pubmed_abstract":["Many real-world image recognition problems, such as diagnostic medical imaging exams, are \"long-tailed\" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a <i>long-tailed</i> and <i>multi-label</i> problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, <b>CXR-LT</b>, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a l"],"journal":["ArXiv"],"pagination":["arXiv:2310.16112v2"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10659524"],"repository":["biostudies-literature"],"pubmed_title":["Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge."],"pmcid":["PMC10659524"],"funding_grant_id":["R01 LM014306"],"pubmed_authors":["Kim C","Kim D","Tran MT","Peng Y","Summers RM","Lu Z","Celi LA","Yamagishi Y","Park W","Verma A","Wang S","Kang M","Wang Z","Jeong J","Jaiswal A","Ryu J","Seo H","Hong F","Zhou Y","Zhuge S","Yang Y","Nguyen-Mau TH","Shih G","Holste G","Lin M"],"additional_accession":[]},"is_claimable":false,"name":"Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.","description":"Many real-world image recognition problems, such as diagnostic medical imaging exams, are \"long-tailed\" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a <i>long-tailed</i> and <i>multi-label</i> problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, <b>CXR-LT</b>, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a l","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2026-05-03T03:23:43.108Z","creation":"2025-02-19T01:52:40.266Z"},"accession":"S-EPMC10659524","cross_references":{"pubmed":["37986726"]}}