Data Augmentation for Medical Imaging: Counterfactual Simulation of Acquisition Parameters via Conditional Diffusion Model

Pedro A. Morão, Yasna Forghani, Nuno Louçã, Pedro Gouveia, Mario A. T. Figueiredo, Joao Santinha
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:1164-1180, 2026.

Abstract

Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual medical images that simulate different IAP without altering patient anatomy. We demonstrate that using these counterfactual images for magnetic resonance (MR) data augmentation can improve segmentation accuracy in out-of-distribution settings, enhancing the overall generalizability and robustness of DL models across diverse imaging conditions. Our approach shows promise in addressing domain and covariate shifts in medical imaging. The code is publicly available at https://github.com/pedromorao/Counterfactual-MRI-Data-Augmentation

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-morao26a, title = {Data Augmentation for Medical Imaging: Counterfactual Simulation of Acquisition Parameters via Conditional Diffusion Model}, author = {Mor\~{a}o, Pedro A. and Forghani, Yasna and Lou\c{c}\~{a}, Nuno and Gouveia, Pedro and Figueiredo, Mario A. T. and Santinha, Joao}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {1164--1180}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/morao26a/morao26a.pdf}, url = {https://proceedings.mlr.press/v301/morao26a.html}, abstract = {Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual medical images that simulate different IAP without altering patient anatomy. We demonstrate that using these counterfactual images for magnetic resonance (MR) data augmentation can improve segmentation accuracy in out-of-distribution settings, enhancing the overall generalizability and robustness of DL models across diverse imaging conditions. Our approach shows promise in addressing domain and covariate shifts in medical imaging. The code is publicly available at https://github.com/pedromorao/Counterfactual-MRI-Data-Augmentation} }
Endnote
%0 Conference Paper %T Data Augmentation for Medical Imaging: Counterfactual Simulation of Acquisition Parameters via Conditional Diffusion Model %A Pedro A. Morão %A Yasna Forghani %A Nuno Louçã %A Pedro Gouveia %A Mario A. T. Figueiredo %A Joao Santinha %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-morao26a %I PMLR %P 1164--1180 %U https://proceedings.mlr.press/v301/morao26a.html %V 301 %X Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual medical images that simulate different IAP without altering patient anatomy. We demonstrate that using these counterfactual images for magnetic resonance (MR) data augmentation can improve segmentation accuracy in out-of-distribution settings, enhancing the overall generalizability and robustness of DL models across diverse imaging conditions. Our approach shows promise in addressing domain and covariate shifts in medical imaging. The code is publicly available at https://github.com/pedromorao/Counterfactual-MRI-Data-Augmentation
APA
Morão, P.A., Forghani, Y., Louçã, N., Gouveia, P., Figueiredo, M.A.T. & Santinha, J.. (2026). Data Augmentation for Medical Imaging: Counterfactual Simulation of Acquisition Parameters via Conditional Diffusion Model. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:1164-1180 Available from https://proceedings.mlr.press/v301/morao26a.html.

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