biostudies-literatureUnknownHolste GNLM NIH HHSMany 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 lArXivarXiv:2310.16112v2https://www.ebi.ac.uk/biostudies/studies/S-EPMC10659524biostudies-literatureTowards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.PMC10659524R01 LM014306Kim CKim DTran MTPeng YSummers RMLu ZCeli LAYamagishi YPark WVerma AWang SKang MWang ZJeong JJaiswal ARyu JSeo HHong FZhou YZhuge SYang YNguyen-Mau THShih GHolste GLin MfalseTowards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.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 l2024-01-01T00:00:00Z2024 Apr2026-05-03T03:23:43.108Z2025-02-19T01:52:40.266ZS-EPMC1065952437986726