解决胸部X光分类中罕见病和未知病的标注不足问题
Handling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning
- 针对长尾分布设计抗不平衡多标签学习方法
- 在36类疾病上实现高精度零样本识别，宏平均mAP领先
- 适合医疗图像中少标注或新病种识别场景
临床胸部X光（CXR）分类常受限于不完全标注，主要源于（i）极端长尾多标签疾病分布，以及（ii）罕见或未见病灶缺乏标注。本文基于PadChest数据集构建36类标签空间，其中30类用于训练，6类用于零样本评估。针对任务1（长尾多标签分类），采用抗不平衡的多标签学习策略，提升尾部类别识别能力，同时保持常见病表现稳定；针对任务2（零样本外部分布识别），提出无需使用任何目标类别监督信号或样本的预测方法。在宏平均平均精度（mAP）指标下，方法在开发阶段公开排行榜上排名第一。代码与预训练模型已开源。
原文摘要 · Abstract (English)
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.
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