发表机构
Rice University; University of Washington（莱斯大学; 华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对CT检查中特定器官表征需求，提出OrganLens通过自监督学习，用器官标识调节共享编码器，经器官特异性蒸馏等方法获取器官特异性表征，在多数据集评估中提升指标，提供了可扩展方法和 reusable 框架。
AI 中文摘要
CT检查会捕捉多个器官，但许多生物医学问题关注特定器官的异常、预后或纵向变化，这需要在同一CT体积内为每个器官单独表征。现有CT基础模型通常生成单个体积级表征，近期解剖学感知方法存在不足。我们引入OrganLens通过自监督进行器官特异性表征学习，一个器官标识调节共享CT编码器，器官特异性蒸馏和解剖掩码监督塑造特征以进行解剖加权池化得到器官特异性表征。推理时共享模型可产生11个器官特异性表征。我们在多个数据集上评估，结果显示其能提升相关指标，为器官特异性CT表征学习提供了可扩展方法，也为医学研究社区提供了可重复使用框架。
英文摘要
A CT examination captures multiple organs, but many biomedical questions concern abnormalities, prognosis, or longitudinal change in a specific organ. These questions require a separate representation for each organ within the same CT volume. Existing CT foundation models commonly produce a single volume-level representation, while recent anatomy-aware methods either encode pre-separated organ volumes or explicitly disentangle images into organ token groups. The former may remove clinically relevant surrounding context, while the latter does not condition a shared encoder on a selected organ before its features are formed. We introduce OrganLens for organ-specific representation learning through self-supervision. An organ identity conditions a shared CT encoder, while organ-specific distillation and anatomy-mask supervision shape features for anatomy-weighted pooling into organ-specific representations. At inference, the shared model produces 11 organ-specific representations without external segmentation masks. We evaluate OrganLens on CT-RATE, RAD-ChestCT, INSPECT, and NLST across diverse acquisitions and downstream evaluations. Relative to CT-pretrained DINOv2, heart representations raise CT-RATE cardiomegaly AUROC from 0.910 to 0.953, while lung representations improve the Harrell C-index for NLST lung-cancer mortality by 14.2\%. The global representation reaches INSPECT Recall@10 of 33.09\% and 32.04\% for text-to-image and image-to-text retrieval, respectively. Across organ-related tasks, anatomically matched representations provide stronger task-relevant signal, while the global representation retains broad utility. OrganLens offers a scalable approach to organ-specific CT representation learning with a shared encoder. More broadly, it provides the medical research community with a reusable framework for studying organ-specific disease across cohorts and clinical endpoints.
Comments16 pages, 7 figures, 5 tables
