ArticleData augmentation, labelling, and imperfections : second MICCAI workshop, DALI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. DALI (Workshop) (2nd : 2022 : Singapore)2022
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study.
Article in Data augmentation, labelling, and imperfections : second MICCAI workshop, DALI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. DALI (Workshop) (2nd : 2022 : Singapore), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- CXR-LT 2026 challenge: Multi-center long-tailed and zero shot chest X-ray classification.Medical image analysis · 2026Article
- CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification.ArXiv · 2026Article
- OVERVIEW OF THE CXR-LT 2026 CHALLENGE: MULTI-CENTER LONG-TAILED AND ZERO SHOT CHEST X-RAY CLASSIFICATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- RadGazeGen: radiomics and gaze-guided chest X-ray generation using diffusion models.Journal of medical imaging (Bellingham, Wash.) · 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
- IHRAS: Automated Medical Report Generation from Chest X-Rays via Classification, Segmentation, and LLMs.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.Medical image analysis · 2024Article
- Synthetically enhanced: unveiling synthetic data's potential in medical imaging research.EBioMedicine · 2024Article
- Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.ArXiv · 2024Article
- How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Feasibility of Using the Privacy-preserving Large Language Model Vicuna for Labeling Radiology Reports.Radiology · 2023Article
- The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups.Radiology. Artificial intelligence · 2023Article
- Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.Bioengineering (Basel, Switzerland) · 2023Article
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8 authors.
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Abstract
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual presentation of rare conditions by studying a few representative examples, teaching a machine to learn from such a "long-tailed" distribution is much more difficult, as standard methods would be easily biased toward the most frequent classes. In this paper, we present a comprehensive benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. To accomplish this, we introduce a challenging new long-tailed chest X-ray benchmark to facilitate research on developing long-tailed learning methods for medical image classification. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images. We evaluate both standard and state-of-the-art long-tailed learning methods on this new benchmark, analyzing which aspects of these methods are most beneficial for long-tailed medical image classification and summarizing insights for future algorithm design. The datasets, trained models, and code are available at https://github.com/VITA-Group/LongTailCXR.
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