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.

翻译：本挑战赛旨在解决已知胸部X光（CXR）病灶的多标签分类以及未知病灶的零样本分类问题。为应对多样化的CXR投影，我们通过分类网络将投影特定模型整合到一个统一框架中。针对零样本分类任务（任务2），我们扩展了CheXzero模型，提出了一种新颖的双分支架构，该架构结合了对比学习、非对称损失（ASL）与大语言模型（LLM）生成的描述性提示词。该方法有效缓解了严重的长尾不平衡问题，并最大化了零样本泛化能力。此外，强数据增强与测试时增强（TTA）确保了两种任务下的鲁棒性。