面向医学视频诊断的临床依据反事实推理
Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosis
用扩散模型合成反事实组织演变视频，并通过时序一致性、病理可分性与反事实对齐三项约束学习表征，为数据稀缺下的视频诊断提供可复用的反事实增强与对比预测范式。
原文依据 a Counterfactual Generator (CG) that synthesizes hypothetical tissue transitions via diffusion modeling
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论临床视频诊断中，医师通过评估各操作阶段组织的动态反应来检测宫颈癌、结直肠癌等疾病，具有关键意义。近期的时空模型直接将视觉演变映射为诊断输出，却忽视了专家推理的两大特征：有临床依据的诊断原则与假设驱动的反事实思维。这一缺口使现有方法将因果性病理线索与非病理变化混为一谈，从而在数据稀缺场景下限制了其可靠性。本文表明，在编码专家诊断原则的临床规则引导下，显式建模反事实组织演变可显著提升诊断鲁棒性，从而模拟临床医生在实践中采用的假设驱动推理。我们提出 MEDVCR，包含：反事实生成器（CG），通过扩散建模合成假设性的组织转变；反事实表征学习（CRL）模块，施加时序一致性、病理可分性与反事实对齐约束；以及双重诊断预测（DDP）策略，将视频级上下文与帧级反事实对比相结合。在两个代表性任务上，MEDVCR 在阴道镜检查上取得 93.0% 的 Recall@1（提升 10.2%），在结肠镜检查上取得 94.8% 的平均精度（AP）（提升 2.6%），优于所有最优基线。我们预期这一反事实推理范式将为基于视觉的临床诊断任务开辟新途径，推动更透明、可解释的决策支持。
英文原文摘要 1604 字符
Clinical video diagnosis, in which physicians assess dynamic tissue responses across procedural stages, is critical for detecting diseases such as cervical and colorectal cancers. Recent spatiotemporal models map visual progressions directly to diagnostic outputs, yet overlook two hallmarks of expert reasoning: clinically grounded diagnostic principles and hypothesis-driven counterfactual thinking. This gap leads existing methods to conflate causal pathological cues with non-pathological variations, thereby limiting their reliability in data-scarce settings. Here we show that explicitly modeling counterfactual tissue evolution, guided by clinical rules that encode expert diagnostic principles, substantially improves diagnostic robustness-emulating the hypothesis-driven reasoning clinicians employ in practice. We introduce MEDVCR, comprising a Counterfactual Generator (CG) that synthesizes hypothetical tissue transitions via diffusion modeling, a Counterfactual Representation Learning (CRL) module that enforces temporal consistency, pathological separability, and counterfactual alignment, and a Dual Diagnostic Prediction (DDP) strategy that combines video-level context with frame-level counterfactual contrast. On two representative tasks, MEDVCR achieves 93.0% Recall@1 on colposcopy (+10.2%) and 94.8% Average Precision (AP) on colonoscopy (+2.6%), outperforming all state-of-the-art (SOTA) baselines. We anticipate that this counterfactual reasoning paradigm will open new avenues for vision-based clinical diagnostic tasks toward more transparent and interpretable decision support.