Time-based Self-supervised Learning for Wireless Capsule Endoscopy

TL;DR

This paper introduces a self-supervised learning approach leveraging temporal structure in wireless endoscopy videos to improve diagnostic detection rates, especially in imbalanced datasets, without requiring labeled data.

Contribution

It presents a novel self-supervised method tailored for wireless endoscopy videos that enhances detection performance without labeled data or class balance adjustments.

Findings

Improved detection rates in medical imaging applications.

Effective handling of class imbalance in datasets.

Self-supervised learning leverages temporal information.

Abstract

State-of-the-art machine learning models, and especially deep learning ones, are significantly data-hungry; they require vast amounts of manually labeled samples to function correctly. However, in most medical imaging fields, obtaining said data can be challenging. Not only the volume of data is a problem, but also the imbalances within its classes; it is common to have many more images of healthy patients than of those with pathology. Computer-aided diagnostic systems suffer from these issues, usually over-designing their models to perform accurately. This work proposes using self-supervised learning for wireless endoscopy videos by introducing a custom-tailored method that does not initially need labels or appropriate balance. We prove that using the inferred inherent structure learned by our method, extracted from the temporal axis, improves the detection rate on several domain-specific applications even under severe imbalance.