Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models

TL;DR

This paper introduces a diffusion-based generative model to synthesize realistic, diverse cataract surgery images, especially rare cases, to improve classifier performance and aid in developing automated surgical assistance systems.

Contribution

The paper presents a novel application of guided diffusion models for generating high-quality synthetic surgical data to address class imbalance in cataract surgery datasets.

Findings

Synthesised samples are indistinguishable from real images by experts.

Synthetic data improves classifier accuracy on rare cases by up to 10%.

The approach provides a reliable source of realistic data for surgical automation.

Abstract

Cataract surgery is a frequently performed procedure that demands automation and advanced assistance systems. However, gathering and annotating data for training such systems is resource intensive. The publicly available data also comprises severe imbalances inherent to the surgical process. Motivated by this, we analyse cataract surgery video data for the worst-performing phases of a pre-trained downstream tool classifier. The analysis demonstrates that imbalances deteriorate the classifier's performance on underrepresented cases. To address this challenge, we utilise a conditional generative model based on Denoising Diffusion Implicit Models (DDIM) and Classifier-Free Guidance (CFG). Our model can synthesise diverse, high-quality examples based on complex multi-class multi-label conditions, such as surgical phases and combinations of surgical tools. We affirm that the synthesised samples display tools that the classifier recognises. These samples are hard to differentiate from real images, even for clinical experts with more than five years of experience. Further, our synthetically extended data can improve the data sparsity problem for the downstream task of tool classification. The evaluations demonstrate that the model can generate valuable unseen examples, allowing the tool classifier to improve by up to 10% for rare cases. Overall, our approach can facilitate the development of automated assistance systems for cataract surgery by providing a reliable source of realistic synthetic data, which we make available for everyone.