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arXiv · 2604.02185

CXR-LT 2026 Challenge: Projection-Aware Multi-Label and Zero-Shot Chest X-Ray Classification

Juno Cho·Dohui Kim·Mingeon Kim·Hyunseo Jang·Chang Sun Lee·Jong Chul Ye
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Abstract

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.

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Juno Cho, Dohui Kim, Mingeon Kim, Hyunseo Jang, Chang Sun Lee, Jong Chul Ye. 2026-04-02. CXR-LT 2026 Challenge: Projection-Aware Multi-Label and Zero-Shot Chest X-Ray Classification. https://arxiv.org/abs/2604.02185

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