Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge - Holste, Gregory; Zhou, Yiliang; Wang, Song; Jaiswal, Ajay; Lin, Mingquan; Zhuge, Sherry; Yang, Yuzhe; Kim, Dongkyun; Nguyen-Mau, Trong Hieu; Tran, Minh Triet; Jeong, Jaehyup; Park, Wongi; et al. (Medical Image Analysis, 2024)
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.
Keywords: Chest radiography; Chest X-ray; Common findings; Condition; Disease classification; Long-tailed learning; Medical image recognition; Multi-label problems; Multi-labels; Real-world image; Algorithms; Humans; Radiographic Image Interpretation, Computer-Assisted; Radiography, Thoracic; Thoracic Diseases; Chest X-ray; Computer-aided diagnosis; Long-tailed learning