Generating healthy counterfactuals with denoising diffusion bridge models

• URL: http://arxiv.org/abs/2510.13684v1
• Date: Wed, 15 Oct 2025 15:40:57 GMT
• Title: Generating healthy counterfactuals with denoising diffusion bridge models
• Authors: Ana Lawry Aguila, Peirong Liu, Marina Crespo Aguirre, Juan Eugenio Iglesias,
• Abstract summary: Denoising diffusion probabilistic models (DDPMs) have become popular methods for generating healthy counterfactuals of pathology data.<n>We propose a novel application of denoising diffusion bridge models (DDBMs)<n>Our DDBM outperforms previously proposed diffusion models and fully supervised approaches at segmentation and anomaly detection tasks.
• Score: 9.788637508976043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating healthy counterfactuals from pathological images holds significant promise in medical imaging, e.g., in anomaly detection or for application of analysis tools that are designed for healthy scans. These counterfactuals should represent what a patient's scan would plausibly look like in the absence of pathology, preserving individual anatomical characteristics while modifying only the pathological regions. Denoising diffusion probabilistic models (DDPMs) have become popular methods for generating healthy counterfactuals of pathology data. Typically, this involves training on solely healthy data with the assumption that a partial denoising process will be unable to model disease regions and will instead reconstruct a closely matched healthy counterpart. More recent methods have incorporated synthetic pathological images to better guide the diffusion process. However, it remains challenging to guide the generative process in a way that effectively balances the removal of anomalies with the retention of subject-specific features. To solve this problem, we propose a novel application of denoising diffusion bridge models (DDBMs) - which, unlike DDPMs, condition the diffusion process not only on the initial point (i.e., the healthy image), but also on the final point (i.e., a corresponding synthetically generated pathological image). Treating the pathological image as a structurally informative prior enables us to generate counterfactuals that closely match the patient's anatomy while selectively removing pathology. The results show that our DDBM outperforms previously proposed diffusion models and fully supervised approaches at segmentation and anomaly detection tasks.

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