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.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/DGM4MICCAI_010.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/profile?id=~Andrea_Posada1
BibTex
@InProceedings{PosAnd_When_MICCAISAT2026,
author = { Posada, Andrea AND Karbole, Wenke AND Doan, Bach Ngoc AND Weers, Alexander AND Abdolrahimzadeh, Solmaz AND Patsiamanidi, Maria AND Haider, Kahkashan AND Khare, Vaishali AND Rueckert, Daniel AND Lotery, Andrew AND Sivaprasad, Sobha AND Menten, Martin J.},
title = { { When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17258},
month = {pending},
page = {pending}
}