Towards Application Aligned Synthetic Surgical Image Synthesis

2025-09-23

AI Summary

The scarcity of annotated surgical images severely limits deep learning applications in computer-assisted interventions, while existing diffusion models suffer from data memorization, resulting in insufficient sample diversity and weak task relevance. To address this, we propose a task-aware synthetic image generation framework that introduces, for the first time, a lightweight fine-tuning mechanism based on preference/non-preference sample pairs. This mechanism explicitly aligns diffusion model outputs with downstream classification and segmentation objectives and supports iterative refinement to progressively enhance synthesis quality. Evaluated on three surgical datasets, our method achieves substantial improvements: classification accuracy increases by 7–9%, and segmentation Dice scores improve by 2–10%, with particularly pronounced gains for rare classes. Iterative optimization further yields additional performance gains of 4–10%.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Addressing surgical data scarcity for deep learning systems
• Overcoming data memorization in diffusion models for surgery
• Aligning synthetic image generation with downstream task objectives

Innovation

• Aligns diffusion models with downstream model preferences
• Uses lightweight fine-tuning on preferred image pairs
• Iteratively refines synthetic samples to boost performance