ArticleMedical image analysis2024
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.
Article in Medical image analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- CXR-LT 2026 challenge: Multi-center long-tailed and zero shot chest X-ray classification.Medical image analysis · 2026Article
- Continual Learning in Medical Image Analysis: Appropriate Use and Lifecycle Governance.Journal of the Korean Society of Radiology · 2026Review
- Knee Osteoarthritis Severity Grading Using Contrastive Learning Image Pre-Training.Journal of personalized medicine · 2026Article
- CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification.ArXiv · 2026Article
- ModernBERT is more efficient than conventional BERT for chest CT findings classification in Japanese radiology reports.Scientific reports · 2026Article
- An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.Scientific reports · 2026Article
- CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray.Medical image analysis · 2025Article
- CXR-MultiTaskNet a unified deep learning framework for joint disease localization and classification in chest radiographs.Scientific reports · 2025Article
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25 authors.
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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.
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