Towards Application Aligned Synthetic Surgical Image Synthesis
This addresses data scarcity for developing deep learning systems in computer-assisted surgical interventions, with incremental improvements over existing methods.
The paper tackles the problem of data memorization in diffusion models for surgical image synthesis, which can harm downstream performance, by introducing a framework that aligns synthetic images with downstream model preferences, resulting in gains of 7-9% in classification and 2-10% in segmentation tasks.
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.