From 6585e06a01930b2ddc4fa3d97a98cd630b2aaa6d Mon Sep 17 00:00:00 2001 From: Favour Nerrise Date: Thu, 2 Nov 2023 10:59:21 -0700 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7146a1e..bb22349 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # xGW-GAT -This repository is the official implementation of `xGW-GAT`, an explainable, graph attention network for n-ary, transductive, classification tasks for functional brain connectomes and gait impairment severity. Our associated paper, **"An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment"** has been accepted to MICCAI 2023 and is supported by the MICCAI 2023 STAR award. Check out our [paper]([https://arxiv.org/abs/2307.13108](https://link.springer.com/chapter/10.1007/978-3-031-43895-0_68)) and our [oral talk](https://youtu.be/ZqoIfHHcIXc)! +This repository is the official implementation of `xGW-GAT`, an explainable, graph attention network for n-ary, transductive, classification tasks for functional brain connectomes and gait impairment severity. Our associated paper, **"An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment"** has been accepted to MICCAI 2023 and is supported by the MICCAI 2023 STAR award. Check out our [paper](https://arxiv.org/abs/2307.13108](https://link.springer.com/chapter/10.1007/978-3-031-43895-0_68) and our [oral talk](https://youtu.be/ZqoIfHHcIXc)! Our pipeline of three modules: 1) A stratified, learning-based sample selection method leveraging Riemannian metrics for connectome similarity comparisons