Time-based Self-supervised Learning for Wireless Capsule Endoscopy
This work addresses challenges in computer-aided diagnostic systems for medical imaging, offering a domain-specific solution that is incremental in applying self-supervised learning to a new context.
The paper tackles the problem of data scarcity and class imbalance in medical imaging by proposing a self-supervised learning method for wireless capsule endoscopy videos, which improves detection rates under severe imbalance without needing initial labels.
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.