{Reference Type}: Journal Article
{Title}: Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.
{Author}: Holste G;Zhou Y;Wang S;Jaiswal A;Lin M;Zhuge S;Yang Y;Kim D;Nguyen-Mau TH;Tran MT;Jeong J;Park W;Ryu J;Hong F;Verma A;Yamagishi Y;Kim C;Seo H;Kang M;Celi LA;Lu Z;Summers RM;Shih G;Wang Z;Peng Y;
{Journal}: Med Image Anal
{Volume}: 97
{Issue}: 0
{Year}: 2024 May 31
{Factor}: 13.828
{DOI}: 10.1016/j.media.2024.103224
{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 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 long-tailed, 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.