让医学影像的假设性修改更精准可解释，避免误改患者特征。
An Interpretable Local Editing Model for Counterfactual Medical Image Generation
- 限定修改区域，只调病变部位，不扰其他特征。
- 生成高质量假想影像，准确率超越现有方法。
- 提供可视化编辑指引图，适合临床可信AI开发。
反事实医学影像生成已成为增强医疗AI系统的重要工具，用于回答“如果……会怎样”类问题。然而现有方法存在两大根本缺陷：一是无法避免无意修改，导致仅调整疾病特征时却连带改变了人口统计属性；二是编辑过程缺乏可解释性，严重限制其在真实医疗场景中的应用。为此，我们提出InstructX2X，一种新型可解释的局部编辑模型，具备区域特异性编辑能力。该方法将修改范围限制在特定区域，有效防止意外变化，同时生成引导图，提供内在可解释的视觉说明。此外，我们构建了MIMIC-EDIT-INSTRUCTION数据集，基于专家验证的医学VQA对生成反事实影像。大量实验表明，InstructX2X在所有主要评估指标上均达到顶尖水平，成功生成高质量的假想胸片，并附带可解释性说明。
原文摘要 · Abstract (English)
Counterfactual medical image generation have emerged as a critical tool for enhancing AI-driven systems in medical domain by answering "what-if" questions. However, existing approaches face two fundamental limitations: First, they fail to prevent unintended modifications, resulting collateral changes in demographic attributes when only disease features should be affected. Second, they lack interpretability in their editing process, which significantly limits their utility in real-world medical applications. To address these limitations, we present InstructX2X, a novel interpretable local editing model for counterfactual medical image generation featuring Region-Specific Editing. This approach restricts modifications to specific regions, effectively preventing unintended changes while simultaneously providing a Guidance Map that offers inherently interpretable visual explanations of the editing process. Additionally, we introduce MIMIC-EDIT-INSTRUCTION, a dataset for counterfactual medical image generation derived from expert-verified medical VQA pairs. Through extensive experiments, InstructX2X achieve state-of-the-art performance across all major evaluation metrics. Our model successfully generates high-quality counterfactual chest X-ray images along with interpretable explanations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。