可解释的局部编辑模型用于反事实医学图像生成
An Interpretable Local Editing Model for Counterfactual Medical Image Generation
AI总结：
本文提出InstructX2X模型，通过区域特定编辑实现反事实医学图像生成，提供可解释的编辑过程和高质量图像生成。
摘要：
反事实医学图像生成已成为增强医疗领域AI驱动系统的关键工具，通过回答
英文摘要：
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.
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。
