Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer Vision

TL;DR

This paper investigates how dataset diversity affects self-supervised learning in surgical computer vision, showing that more diverse data improves model performance across multiple surgical tasks.

Contribution

It demonstrates that increasing dataset diversity in SSL pretraining significantly enhances surgical computer vision model performance compared to procedure-specific data alone.

Findings

Procedure-specific data improves performance by up to 36.8% over ImageNet.

Adding heterogeneous surgical data further boosts accuracy by up to 5.2%.

Diversity in training data is beneficial for SSL in surgical applications.

Abstract

Over the past decade, computer vision applications in minimally invasive surgery have rapidly increased. Despite this growth, the impact of surgical computer vision remains limited compared to other medical fields like pathology and radiology, primarily due to the scarcity of representative annotated data. Whereas transfer learning from large annotated datasets such as ImageNet has been conventionally the norm to achieve high-performing models, recent advancements in self-supervised learning (SSL) have demonstrated superior performance. In medical image analysis, in-domain SSL pretraining has already been shown to outperform ImageNet-based initialization. Although unlabeled data in the field of surgical computer vision is abundant, the diversity within this data is limited. This study investigates the role of dataset diversity in SSL for surgical computer vision, comparing procedure-specific datasets against a more heterogeneous general surgical dataset across three different downstream surgical applications. The obtained results show that using solely procedure-specific data can lead to substantial improvements of 13.8%, 9.5%, and 36.8% compared to ImageNet pretraining. However, extending this data with more heterogeneous surgical data further increases performance by an additional 5.0%, 5.2%, and 2.5%, suggesting that increasing diversity within SSL data is beneficial for model performance. The code and pretrained model weights are made publicly available at https://github.com/TimJaspers0801/SurgeNet.