Chest X-Ray (CXR) classification in clinical practice is often limited by imperfect supervision, arising from (i) extreme long-tailed multi-label disease distributions and (ii) missing annotations for rare or previously unseen findings. The CXR-LT 2026 challenge addresses these issues on a PadChest-based benchmark with a 36-class label space split into 30 in-distribution classes for training and 6 out-of-distribution (OOD) classes for zero-shot evaluation. We present task-specific solutions tailored to the distinct supervision regimes. For Task 1 (long-tailed multi-label classification), we adopt an imbalance-aware multi-label learning strategy to improve recognition of tail classes while maintaining stable performance on frequent findings. For Task 2 (zero-shot OOD recognition), we propose a prediction approach that produces scores for unseen disease categories without using any supervised labels or examples from the OOD classes during training. Evaluated with macro-averaged mean Average Precision (mAP), our method achieves strong performance on both tasks, ranking first on the public leaderboard of the development phase. Code and pre-trained models are available at https://github.com/hieuphamha19/CXR_LT.
@article{arxiv.2602.13430,
title = {Handling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning},
author = {Ha-Hieu Pham and Hai-Dang Nguyen and Thanh-Huy Nguyen and Min Xu and Ulas Bagci and Trung-Nghia Le and Huy-Hieu Pham},
journal= {arXiv preprint arXiv:2602.13430},
year = {2026}
}