Open AccessExplainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation
Author(s) -
Awadelrahman M. A. Ahmed,
Leen A. M. Ali
Publication year - 2021
Publication title -
nordic machine intelligence
Language(s) - English
Resource type - Journals
ISSN - 2703-9196
DOI - 10.5617/nmi.9126
Subject(s) - segmentation , jaccard index , artificial intelligence , relevance (law) , computer science , image segmentation , image (mathematics) , adversarial system , generative grammar , layer (electronics) , pattern recognition (psychology) , computer vision , pixel , chemistry , organic chemistry , political science , law
This paper contributes in automating medical image segmentation by proposing generative adversarial network based models to segment both polyps and instruments in endoscopy images. A main contribution of this paper is providing explanations for the predictions using layer-wise relevance propagation approach, showing which pixels in the input image are more relevant to the predictions. The models achieved 0.46 and 0.70, on Jaccard index and 0.84 and 0.96 accuracy, on the polyp segmentation and the instrument segmentation, respectively.