Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation
DOI:
https://doi.org/10.5617/nmi.9126Keywords:
medical image segmentation, explainable artificialintelligence, generative adversarial networks, layer-wise relevance propagationAbstract
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.
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Published
2021-11-01
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NMI Challenge