用反事实推理提升医学视频诊断准确率
Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosis
- 通过扩散模型生成假设病理下的组织演变过程
- 在宫颈镜与肠镜数据上提升2.6%至10.2%性能
- 适合临床决策支持系统研究者使用
医学视频诊断需从检查过程中动态组织反应推断临床决策。现有方法依赖端到端学习，存在重外观轻病理、缺乏临床先验、仅基于观测无反事实比较等问题。本文提出MedVCR框架，模拟临床诊断思维：包含基于扩散的反事实生成器，用于合成特定病理状态下的组织演化；反事实表征学习模块，通过时间一致性、病灶可分性与反事实对齐等临床规则编码诊断知识；双路径诊断策略，融合视频级评估与帧级反事实分析。在全监督（如宫颈镜）和弱监督（如肠镜）场景下，相比领先基线提升2.6%~10.2%。全面消融实验验证各组件有效性。代码将公开。
原文摘要 · Abstract (English)
Medical video diagnosis involves inferring clinical decisions from dynamic tissue responses throughout examination processes. Existing methods rely on an end-to-end learning paradigm that i) focuses on appearance rather than pathology, ii) lacks clinical priors, and iii) reasons solely from observations without counterfactual comparison. This work introduces MedVCR, a counterfactual reasoning framework that mimics clinical diagnostic thinking. MedVCR comprises three components: a Counterfactual Generator that synthesizes tissue evolution under specified pathological states via a diffusion-based manner; a Counterfactual Representation Learning module that encodes diagnostic knowledge through clinical rules (i.e., temporal consistency, pathological separability, and counterfactual alignment); and a Dual Diagnostic Prediction strategy that integrates video-level assessment with frame-level counterfactual analysis. MedVCR is evaluated under both fully supervised (e.g., colposcopy) and weakly supervised (e.g., colonoscopy) video diagnosis settings, yielding 2.6%-10.2% performance gains compared with leading baselines. Comprehensive ablation studies further validate the effectiveness of each component. The code will be released.
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