ARC-CT用LLM提取标签替代人工标注，在小型模型上超越大型transformer，解决小病灶和共享异常问题。
ARC-CT是一种新型区域感知框架，解决了胸部CT对比学习的两大挑战。该模型使用3D ResNet-18主干网络，在18种异常分类上达到0.86的无掩码宏AUC。AnatomyQFormer通过自动生成的器官掩码约束查询来定位证据，label-Jaccard软InfoNCE目标减少了共享临床发现研究间的假阴性惩罚，器官级对齐损失连接掩码池化视觉特征与器官特异性报告文本。
ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT
Contrastive vision-language learning uses paired chest CT volumes and radiology reports to learn abnormality classifiers without manually annotated labels. However, two characteristics of chest CT challenge conventional global contrastive learning. First, many critical abnormalities are small or anatomically localized, and pooling an en- tire volume into a single embedding may dilute their visual evidence. Second, the standard contrastive objective treats every other scan in a batch as a negative. Because many chest CTs share abnormalities, this objective incorrectly pushes co-positive pairs apart. We propose Anatomy-Routed Contrastive Learning for 3D Chest CT (ARC-CT), a region-aware framework that addresses these limitations using only la- bels extracted from reports by an LLM, with no manual annotations or bounding boxes. ARC-CT combines three components: (1) an Anato- myQFormer localizing evidence via queries constrained by automatically generated organ masks; (2) a label-Jaccard soft InfoNCE objective in- tegrating the standard one-hot target with the label-set overlap of each pair, which reduces false-negative penalties between studies that share clinical findings; and (3) an organ-level alignment loss connecting mask- pooled visual features to organ-specific report text extracted offline with a large language model. ARC-CT achieves a 0.86 mask-free macro AUC across 18 abnormalities using a compact 3D ResNet-18 backbone. Over- all, ARC-CT outperforms both comparable efficient baselines and sev- eral larger transformer models. Our code and weights are available at https://github.com/arc-ct/arc-ct.