{"ID":2867029,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18796","arxiv_id":"2509.18796","title":"Towards Application Aligned Synthetic Surgical Image Synthesis","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.","short_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 e...","url_abs":"https://arxiv.org/abs/2509.18796","url_pdf":"https://arxiv.org/pdf/2509.18796v1","authors":"[\"Danush Kumar Venkatesh\",\"Stefanie Speidel\"]","published":"2025-09-23T08:40:40Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}