Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies
Journal:
arXiv
Published Date:
Apr 8, 2025
Abstract
Capsule endoscopy is a method to capture images of the gastrointestinal tract
and screen for diseases which might remain hidden if investigated with standard
endoscopes. Due to the limited size of a video capsule, embedding AI models
directly into the capsule demands careful consideration of the model size and
thus complicates anomaly detection in this field. Furthermore, the scarcity of
available data in this domain poses an ongoing challenge to achieving effective
anomaly detection. Thus, this work introduces an ensemble strategy to address
this challenge in anomaly detection tasks in video capsule endoscopies,
requiring only a small number of individual neural networks during both the
training and inference phases. Ensemble learning combines the predictions of
multiple independently trained neural networks. This has shown to be highly
effective in enhancing both the accuracy and robustness of machine learning
models. However, this comes at the cost of higher memory usage and increased
computational effort, which quickly becomes prohibitive in many real-world
applications. Instead of applying the same training algorithm to each
individual network, we propose using various loss functions, drawn from the
anomaly detection field, to train each network. The methods are validated on
the two largest publicly available datasets for video capsule endoscopy images,
the Galar and the Kvasir-Capsule dataset. We achieve an AUC score of 76.86% on
the Kvasir-Capsule and an AUC score of 76.98% on the Galar dataset. Our
approach outperforms current baselines with significantly fewer parameters
across all models, which is a crucial step towards incorporating artificial
intelligence into capsule endoscopies.
