When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images

2026-08-24

AI Summary

研究通过三种文本条件编辑方法生成视网膜图像，评估其身份保留情况，发现配对训练方法最能保持患者身份。

Abstract

Counterfactual medical image generation aims to modify an existing image to reflect a hypothetical scenario in which certain characteristics of the imaged subject are altered, while keeping their identity fixed. Most existing works repurpose established image editing methods, which do not directly supervise identity preservation. Instead, they assume that identity is implicitly preserved by anchoring generation to the source image. This assumption is rarely tested and may fail in domains where biometric cues are subtle, such as retinal optical coherence tomography (OCT). In this work, we explicitly measure identity preservation for three groups of text-conditioned editing methods - source-anchored, structured-prompt, and paired-training - using referee classifiers, embedding alignment scores, and a blind reader study. We find that all methods produce high-quality OCT images with comparable editing success, yet their identity preservation differs markedly. Source-anchored editing frequently alters the depicted subject, while paired-training preserves it best. We argue that future work on medical counterfactual generation must explicitly measure and report identity preservation alongside image realism and editing success.

Problem

• counterfactual medical image generation
• identity preservation
• retinal OCT

Innovation

• identity preservation
• counterfactual medical image generation
• embedding alignment scores