From Labels to Priors in Capsule Endoscopy: A Prior Guided Approach for Improving Generalization with Few Labels

TL;DR

This paper introduces a prior-guided contrastive learning method for Wireless Capsule Endoscopy that leverages domain knowledge to improve generalization and reduce labeling needs, achieving state-of-the-art results.

Contribution

The authors propose a novel prior-guided contrastive learning approach that uses domain knowledge as proxies for labels, enhancing pathology classification with fewer annotations.

Findings

Outperforms existing methods in pathology classification accuracy.

Enhances cross-dataset generalization capabilities.

Scales effectively to unseen pathology categories.

Abstract

The lack of generalizability of deep learning approaches for the automated diagnosis of pathologies in Wireless Capsule Endoscopy (WCE) has prevented any significant advantages from trickling down to real clinical practices. As a result, disease management using WCE continues to depend on exhaustive manual investigations by medical experts. This explains its limited use despite several advantages. Prior works have considered using higher quality and quantity of labels as a way of tackling the lack of generalization, however this is hardly scalable considering pathology diversity not to mention that labeling large datasets encumbers the medical staff additionally. We propose using freely available domain knowledge as priors to learn more robust and generalizable representations. We experimentally show that domain priors can benefit representations by acting in proxy of labels, thereby significantly reducing the labeling requirement while still enabling fully unsupervised yet pathology-aware learning. We use the contrastive objective along with prior-guided views during pretraining, where the view choices inspire sensitivity to pathological information. Extensive experiments on three datasets show that our method performs better than (or closes gap with) the state-of-the-art in the domain, establishing a new benchmark in pathology classification and cross-dataset generalization, as well as scaling to unseen pathology categories.