Towards 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 long-tailed and multi -label 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 longtailed, multi -label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT , on long-tailed, multi -label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top -performing solutions, providing practical recommendations for long-tailed, multi -label medical image classification. Finally, we use these insights to propose a path forward involving vision -language foundation models for few- and zero -shot disease classification.