[2603.00423] An Interpretable Local Editing Model for Counterfactual Medical Image Generation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00423 (cs) [Submitted on 28 Feb 2026] Title:An Interpretable Local Editing Model for Counterfactual Medical Image Generation Authors:Hyungi Min, Taeseung You, Hangyeul Lee, Yeongjae Cho, Sungzoon Cho View a PDF of the paper titled An Interpretable Local Editing Model for Counterfactual Medical Image Generation, by Hyungi Min and 4 other authors View PDF HTML (experimental) Abstract: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-v...