Weakly-supervised Convolutional learning for detection of inflammatory gastrointestinal lesions

Abstract

Graphic image annotations provide the necessary ground truth information for supervised machine learning in image-based computer-aided medical diagnosis. Performing such annotations is usually a time-consuming and cost-inefficient process requiring knowledge from domain experts. To cope with this problem we propose a novel weakly-supervised learning method based on a Convolutional Neural Network (CNN) architecture. The advantage of the proposed method over conventional supervised approaches is that only image-level semantic annotations are used in the training process, instead of pixel-level graphic annotations. This can drastically reduce the required annotation effort. Its advantage over the few state-of-the-art weakly-supervised CNN architectures is its simplicity. The performance of the proposed method is evaluated in the context of computer-aided detection of inflammatory gastrointestinal lesions in wireless capsule endoscopy videos. This is a broad category of lesions, for which early detection and treatment can be of vital importance. The results show that the proposed weakly-supervised learning method can be more effective than the conventional supervised learning, with an accuracy of 90%.