Towards Application Aligned Synthetic Surgical Image Synthesis

• URL: http://arxiv.org/abs/2509.18796v1
• Date: Tue, 23 Sep 2025 08:40:40 GMT
• Title: Towards Application Aligned Synthetic Surgical Image Synthesis
• Authors: Danush Kumar Venkatesh, Stefanie Speidel,
• Abstract summary: We introduce emphSurgical Application-Aligned Diffusion (SAADi), a new framework that aligns diffusion models with samples preferred by downstream models.<n>Our method constructs pairs of emphpreferred and emphnon-preferred synthetic images and employs lightweight fine-tuning of diffusion models to align the image generation process with downstream objectives explicitly.
• Score: 3.1373284090264857
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The scarcity of annotated surgical data poses a significant challenge for developing deep learning systems in computer-assisted interventions. While diffusion models can synthesize realistic images, they often suffer from data memorization, resulting in inconsistent or non-diverse samples that may fail to improve, or even harm, downstream performance. We introduce \emph{Surgical Application-Aligned Diffusion} (SAADi), a new framework that aligns diffusion models with samples preferred by downstream models. Our method constructs pairs of \emph{preferred} and \emph{non-preferred} synthetic images and employs lightweight fine-tuning of diffusion models to align the image generation process with downstream objectives explicitly. Experiments on three surgical datasets demonstrate consistent gains of $7$--$9\%$ in classification and $2$--$10\%$ in segmentation tasks, with the considerable improvements observed for underrepresented classes. Iterative refinement of synthetic samples further boosts performance by $4$--$10\%$. Unlike baseline approaches, our method overcomes sample degradation and establishes task-aware alignment as a key principle for mitigating data scarcity and advancing surgical vision applications.

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