Understanding Graph Convolutional Networks to detect Brain Lesions from Stroke
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Brain lesions derived from stroke episodes can result in disabilities for a patient. Therefore, the segmentation of brain lesions is an important task in neurology. Recently this task has been mainly tackled by machine learning approaches that demonstrated to be very successful. One of these approaches is Graph Convolutional Networks (GCN), where the input image is interpreted as a graph structure. As usual for neural networks, the interpretability is hard due to their black-box nature. We provide an interactive visualization of the activation inherent in the GCN, which is map from the original dataset. We visualize the activation values of the underlying graph network on top of the input image. We show the usability of our approach by applying it to a GCN that was trained on a real-world dataset.
Description
@inproceedings{10.2312:vcbm.20221195,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Renata G. Raidou and Björn Sommer and Torsten W. Kuhlen and Michael Krone and Thomas Schultz and Hsiang-Yun Wu},
title = {{Understanding Graph Convolutional Networks to detect Brain Lesions from Stroke}},
author = {Iporre-Rivas, Ariel and Scheuermann, Gerik and Gillmann, Christina},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-177-9},
DOI = {10.2312/vcbm.20221195}
}