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05/02/2023
Multi-Head Graph Convolutional Network for Structural Connectome Classification
We tackle classification based on brain connectivity derived from diffus...
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11/26/2022
Latent Graph Inference using Product Manifolds
Graph Neural Networks usually rely on the assumption that the graph topo...
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07/21/2022
Unsupervised pre-training of graph transformers on patient population graphs
Pre-training has shown success in different areas of machine learning, s...
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04/01/2022
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Graphs are a powerful tool for representing and analyzing unstructured, ...
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03/23/2022
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
Pre-training has shown success in different areas of machine learning, s...
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04/08/2021
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
The increased amount of multi-modal medical data has opened the opportun...
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03/29/2021
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction
Interpretability in Graph Convolutional Networks (GCNs) has been explore...
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02/27/2021
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
Disease prediction is a well-known classification problem in medical app...
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05/14/2020
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
Large-scale population-based studies in medicine are a key resource towa...
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03/27/2020
Latent Patient Network Learning for Automatic Diagnosis
Recently, Graph Convolutional Networks (GCNs) has proven to be a powerfu...
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02/11/2020
Differentiable Graph Module (DGM) Graph Convolutional Networks
Graph deep learning has recently emerged as a powerful ML concept allowi...
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05/08/2019
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
Clinical diagnostic decision making and population-based studies often r...
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05/08/2019
Adaptive image-feature learning for disease classification using inductive graph networks
Recently, Geometric Deep Learning (GDL) has been introduced as a novel a...
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03/11/2019
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Geometric deep learning provides a principled and versatile manner for t...
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02/04/2019
Towards an Interactive and Interpretable CAD System to Support Proximal Femur Fracture Classification
Fractures of the proximal femur represent a critical entity in the weste...
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12/24/2018
Self-Attention Equipped Graph Convolutions for Disease Prediction
Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imagi...
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09/27/2018
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
In this paper, we target the problem of fracture classification from cli...
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04/28/2018
Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction
Structural data from Electronic Health Records as complementary informat...
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12/19/2016