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Found 20 papers, 7 papers with code
On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
In this work, we assess attribution methods from a perspective not previously explored in the graph domain: retraining.
Multi-Head Graph Convolutional Network for Structural Connectome Classification
To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification.
Latent Graph Inference using Product Manifolds
The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated.
Unsupervised pre-training of graph transformers on patient population graphs
We find that our proposed pre-training methods help in modeling the data at a patient and population level and improve performance in different fine-tuning tasks on all datasets.
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation.
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
We test our method on two medical datasets of patient records, TADPOLE and MIMIC-III, including imaging and non-imaging features and different prediction tasks.
Ranked #1 on
Length-of-Stay prediction
on MIMIC-III
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
The soft pseudo-labels are then used to train a deep student network for disease prediction of unseen test data for which the graph modality is unavailable.
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction
The main novelty lies in the interpretable attention module (IAM), which directly operates on multi-modal features.
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
This is accomplished by associating a graph-based neural network to each class, which is responsible for weighting the class samples and changing the importance of each sample for the classifier.
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multigraph Geometric Matrix Completion (MGMC).
Latent-Graph Learning for Disease Prediction
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction.
Differentiable Graph Module (DGM) for Graph Convolutional Networks
We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning).
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal features.
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks
We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly.
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain.
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification.
Self-Attention Equipped Graph Convolutions for Disease Prediction
A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction.
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system.
Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction
Structural data from Electronic Health Records as complementary information to imaging data for disease prediction.
Cross-Modal Manifold Learning for Cross-modal Retrieval
This paper presents a new scalable algorithm for cross-modal similarity preserving retrieval in a learnt manifold space.
