
Research Article
Informative Classification of Capsule Endoscopy Videos Using Active Learning
@INPROCEEDINGS{10.1007/978-3-031-60665-6_23, author={Filipe Fonseca and Beatriz Nunes and Marta Salgado and Augusto Silva and Ant\^{o}nio Cunha}, title={Informative Classification of Capsule Endoscopy Videos Using Active Learning}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={Active learning Deep learning Capsule endoscopy}, doi={10.1007/978-3-031-60665-6_23} }
- Filipe Fonseca
Beatriz Nunes
Marta Salgado
Augusto Silva
António Cunha
Year: 2024
Informative Classification of Capsule Endoscopy Videos Using Active Learning
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_23
Abstract
The wireless capsule endoscopy is a non-invasive imaging method that allows observation of the inner lumen of the small intestine, but with the cost of a longer duration to process its resulting videos. Therefore, the scientific community has developed several machine learning strategies to help reduce that duration. Such strategies are typically trained and evaluated on small sets of images, ultimately not proving to be efficient when applied to full videos. Labelling full Capsule Endoscopy videos requires significant effort, leading to a lack of data on this medical area. Active learning strategies allow intelligent selection of datasets from a vast set of unlabelled data, maximizing learning and reducing annotation costs. In this experiment, we have explored active learning methods to reduce capsule endoscopy videos’ annotation effort by compiling smaller datasets capable of representing their content.