CTIS-QA：基于临床模板的滑动切片级问题回答用于病理学
CTIS-QA: Clinical Template-Informed Slide-level Question Answering for Pathology
AI总结：
CTIS-QA通过双流架构实现滑动切片级问题回答，结合临床模板和视觉-语言对齐，提升病理诊断准确性。
摘要：
本文介绍了一种基于临床诊断模板的流程，以系统地收集和结构化病理信息。在与病理科医生合作并受美国病理学家学会（CAP）癌症协议的指导下，我们设计了一个临床病理报告模板（CPRT），以确保从病理报告中提取诊断元素的全面性和标准化。我们在TCGA-BRCA上验证了该流程的有效性。首先，我们使用CPRT从报告中提取病理特征。这些特征随后用于构建CTIS-Align，一个包含80,000个滑动切片描述对的804个WSI数据集，用于视觉-语言对齐训练，以及CTIS-Bench，一个经过严格编纂的VQA基准测试，包含977个WSI和14,879个问题-答案对。CTIS-Bench强调基于临床的、封闭式问题（例如肿瘤分级、受体状态），反映了真实的诊断流程，最小化非视觉推理，并要求真正的滑动切片理解。我们进一步提出了CTIS-QA，一个滑动切片级问题回答模型，其双流架构模仿了病理科医生的诊断方法。一个流通过基于聚类的特征聚合来捕捉全局滑动切片级上下文，另一个流则通过注意力引导的补丁感知模块专注于显著的局部区域。在WSI-VQA、CTIS-Bench和滑动切片诊断任务上的大量实验表明，CTIS-QA在多个指标上都优于现有最先进的模型。代码和数据可在https://github.com/HLSvois/CTIS-QA上获得。
英文摘要：
In this paper, we introduce a clinical diagnosis template-based pipeline to systematically collect and structure pathological information. In collaboration with pathologists and guided by the the College of American Pathologists (CAP) Cancer Protocols, we design a Clinical Pathology Report Template (CPRT) that ensures comprehensive and standardized extraction of diagnostic elements from pathology reports. We validate the effectiveness of our pipeline on TCGA-BRCA. First, we extract pathological features from reports using CPRT. These features are then used to build CTIS-Align, a dataset of 80k slide-description pairs from 804 WSIs for vision-language alignment training, and CTIS-Bench, a rigorously curated VQA benchmark comprising 977 WSIs and 14,879 question-answer pairs. CTIS-Bench emphasizes clinically grounded, closed-ended questions (e.g., tumor grade, receptor status) that reflect real diagnostic workflows, minimize non-visual reasoning, and require genuine slide understanding. We further propose CTIS-QA, a Slide-level Question Answering model, featuring a dual-stream architecture that mimics pathologists' diagnostic approach. One stream captures global slide-level context via clustering-based feature aggregation, while the other focuses on salient local regions through attention-guided patch perception module. Extensive experiments on WSI-VQA, CTIS-Bench, and slide-level diagnostic tasks show that CTIS-QA consistently outperforms existing state-of-the-art models across multiple metrics. Code and data are available at https://github.com/HLSvois/CTIS-QA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
