# Generic Feature Learning for Wireless Capsule Endoscopy Analysis

arXiv: https://arxiv.org/abs/1607.07604 · HTML: https://mlsanity.com/paper/1607.07604/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2016.

## Built on by (most cited first, 5 of 5)

- [Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy](https://mlsanity.com/paper/2106.16162/) (2021)
- [Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey](https://mlsanity.com/paper/2605.06714/) (2026)
- [Time-based Self-supervised Learning for Wireless Capsule Endoscopy](https://mlsanity.com/paper/2204.09773/) (2022)
- [Learning Visual Representations with Optimum-Path Forest and its Applications to Barrett's Esophagus and Adenocarcinoma Diagnosis](https://mlsanity.com/paper/2101.07209/) (2021)
- [A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images](https://mlsanity.com/paper/2207.09769/) (2022)

## Abstract (arXiv)

The interpretation and analysis of the wireless capsule endoscopy recording is a complex task which requires sophisticated computer aided decision (CAD) systems in order to help physicians with the video screening and, finally, with the diagnosis. Most of the CAD systems in the capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, new CAD system has to be designed from scratch. This characteristic makes the design of new CAD systems a very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which avoids the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed by using state of the art hand-crafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).