CXR-LT 2026 Challenge: Projection-Aware Multi-Label and Zero-Shot Chest X-Ray Classification
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Abstract (arXiv)
This challenge tackles multi-label classification for known chest X-ray (CXR) lesions and zero-shot classification for unseen ones. To handle diverse CXR projections, we integrate projection-specific models via a classification network into a unified framework. For zero-shot classification (Task 2), we extend CheXzero with a novel dual-branch architecture that combines contrastive learning, Asymmetric Loss (ASL), and LLM-generated descriptive prompts. This effectively mitigates severe long-tail imbalances and maximizes zero-shot generalization. Additionally, strong data and test-time augmentations (TTA) ensure robustness across both tasks.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 21 references that are in the map. First posted on arXiv 2 Apr 2026.
Builds on
17 of 21 references.
References · 17
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5 more
- 2022What does a platypus look like? Generating customized prompts for zero-shot image classificationcited by 267
- 2020Weakly Supervised Lesion Localization With Probabilistic-CAM Poolingcited by 36
- 2024BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Modelscited by 28
- 2021Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantificationcited by 21
- 2026Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classificationcited by 3
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Citing papers per year: 2026: 1.
1 paper.