AHA/ASA Journals

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...
IEEE

SSD: Self-Supervised Distillation for Heterophilic Graph Representation Learning

Abstract: Graph Knowledge Distillation (GKD) has made remarkable progress in graph representation learning in recent years. Despite its great success, GKD often obeys the label-dependence manner, ...
publichealth.jhu

Biostatistics Dept. Seminar: Graph Semi-Supervised Learning for Point Classification on Data Manifolds

Abstract: Many modern classification problems involve data that live in high-dimensional spaces but exhibit strong low-dimensional structure. Motivated by the manifold hypothesis, this talk presents a ...
GitHub

Graph Positional Autoencoders as Self-supervised Learners (GraphPAE)

accelerate==0.33.0 numpy==1.24.4 ogb==1.3.6 pandas==2.2.3 PyYAML==6.0.2 rdkit_pypi==2022.9.5 scikit_learn==1.3.2 scipy==1.8.1 torch==2.1.2+cu121 torch_geometric==2.6.1 torch_scatter==2.1.2+pt21cu121 ...
IEEE

Self-Supervised Learning with Adaptive Graph Modeling for EEG-Based Epileptic Seizure Classification

Abstract: Objective: Epileptic seizure classification using EEG signals remains a significant challenge due to complex spatial-temporal dependencies, limited labeled data, and severe class imbalance.
Frontiers

Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph

Lung cancer remains a leading cause of global cancer mortality, demanding precise diagnostic tools for accurate subtype classification. This paper introduces a novel Enhanced GraphSAGE (E-GraphSAGE) ...