Generic Feature Learning for Wireless Capsule Endoscopy Analysis
This addresses the time-consuming need to design new systems from scratch for each clinical application in capsule endoscopy, though it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackles the problem of designing new computer-aided decision systems for wireless capsule endoscopy by introducing a deep convolutional neural network that avoids hand-crafted features, achieving a mean classification accuracy of 96% for six intestinal motility events, a 14% relative improvement over state-of-the-art methods.
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).