Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification

Hexin Dong; Yi Lin; Pengyu Zhou; Xuan Zhong Feng; Alan Clint Legasto; Mingquan Lin; Hao Chen; Yuzhe Yang; George Shih; Yifan Peng

arXiv · 2602.22092

Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification

Hexin Dong·Yi Lin·Pengyu Zhou·Xuan Zhong Feng·Alan Clint Legasto·Mingquan Lin·Hao Chen·Yuzhe Yang·George Shih·Yifan Peng

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Abstract

Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on closed-set classes from single institutions, failing to capture the prevalence of rare diseases or the appearance of novel findings. To address this, we present the CXR-LT 2026 challenge. This third iteration of the benchmark introduces a multi-center dataset comprising over 145,000 images from PadChest and NIH Chest X-ray datasets. The challenge defines two core tasks: (1) Robust Multi-Label Classification on 30 known classes and (2) Open-World Generalization to 6 unseen (out-of-distribution) rare disease classes. We report the results of the top-performing teams, evaluating them via mean Average Precision (mAP), AUROC, and F1-score. The winning solutions achieved an mAP of 0.5854 on Task 1 and 0.4315 on Task 2, demonstrating that large-scale vision-language pre-training significantly mitigates the performance drop typically associated with zero-shot diagnosis.

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Hexin Dong, Yi Lin, Pengyu Zhou, Xuan Zhong Feng, Alan Clint Legasto, Mingquan Lin, Hao Chen, Yuzhe Yang, George Shih, Yifan Peng. 2026-02-25. Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification. https://arxiv.org/abs/2602.22092

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