Alternatives and similar repositories for tuto-dl-on-graphs

Users that are interested in tuto-dl-on-graphs are comparing it to the libraries listed below

• XC-NCTR / AnimalGAN
  A Generative Adversarial Network Model Alternative to Animal Studies for Clinical Pathology Assessment
  ☆11Updated last year
• raghavian / graph_refinement
  This is official Pytorch implementation of "Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks"…
  ☆33Updated 2 years ago
• zhangxiaoyu11 / OmiTrans
  The first GANs-based omics-to-omics translation framework
  ☆13Updated 2 years ago
• hrlblab / SimTriplet
  SimTriplet: PyTorch Implementation
  ☆27Updated 4 years ago
• zhchs / Disease-Prediction-via-GCN
  A simple implementation of the paper "Disease Prediction via Graph Neural Networks".
  ☆22Updated 4 years ago
• HakimBenkirane / Hyper-adaC
  Hyper-AdaC: Adaptive clustering-based hypergraph representation of whole slide images for survival analysis
  ☆15Updated 2 years ago
• histocartography / athena
  Computational Framework to Analyse Spatial Heterogeneity in Visum, IMC or MIBI data sets
  ☆12Updated 3 years ago
• Pseudomanifold / pyper
  Python-based persistent homology algorithms
  ☆19Updated 2 years ago
• gevaertlab / metalearning_survival
  ☆16Updated 4 years ago
• histocartography / patho-quant-explainer
  Code for replicating results presented in the paper: "Quantifying Explainers of Graph Neural Networks in Computational Pathology"
  ☆45Updated 4 years ago
• iDEA-iSAIL-Lab-UIUC / pyg-ssl
  Graph Self-Supervised Learning Toolkit
  ☆43Updated 3 months ago
• alonalj / PathoMCH
  ☆21Updated 3 years ago
• CellProfiling / HPA-competition
  Human Protein Atlas image classification competition
  ☆13Updated 2 years ago
• xinglab-ai / mda
  Revealing Hidden Patterns in Deep Neural Network Feature Space Continuum via Manifold Learning (Nature Communications, 2023)
  ☆28Updated last year
• urchade / semi-supervised-learning
  Semi-supervised learning tutorial in pytorch (pseudio label, Pi model, mean teacher, FixMatch, UDA)
  ☆15Updated 4 years ago
• pievos101 / GNN-SubNet
  Disease subnetwork detection with explainable Graph Neural Networks
  ☆25Updated last year
• hybrid-kg / clep
  🤖 A Python Package for generating new patient representations driven by data and prior knowledge
  ☆24Updated 3 months ago
• KostiukIvan / Multiple-instance-learning-with-graph-neural-networks
  Multiple instance learning with graph neural networks.
  ☆30Updated 4 years ago
• shaoweinuaa / HM2TFS
  Hyper-graph based Multi-task Feature Selection for Multi-modal Classification of Alzheimer's Disease
  ☆12Updated 6 years ago
• basiralab / MultiGraphGAN
  MultiGraphGAN for predicting multiple target graphs from a source graph using geometric deep learning.
  ☆22Updated 2 years ago
• hwanglab / TILs_Analysis
  Pan-cancer tils analysis study by Hongming Xu
  ☆14Updated 4 years ago
• NYUMedML / conditional_ssl_hist
  ☆14Updated 3 years ago
• ChristianMarzahl / WsiRegistration
  Robust quad-tree based registration on whole slide images
  ☆21Updated 6 months ago
• Tombaugh / CapSurv
  CapSurv: Capsule Network for Survival Analysis With Whole Slide Pathological Images
  ☆10Updated 6 years ago
• jocpae / VesselGraph
  ☆121Updated 6 months ago
• zhangxiaoyu11 / OmiEmbed
  Multi-task deep learning framework for multi-omics data analysis
  ☆48Updated 3 years ago
• JunMa11 / ML-SRT
  Machine learning methods for spatially resolved transcriptomics with histology images: a collection of related resources.
  ☆10Updated 3 years ago
• Boehringer-Ingelheim / anomaly-detection-in-histology
  Learning image representations for anomaly detection: application to discovery of histological alterations in drug development
  ☆21Updated last year
• ashleve / graph_classification
  Benchmarking GNNs with PyTorch Lightning: Open Graph Benchmarks and image classification from superpixels
  ☆30Updated 3 years ago
• VITA-Group / L2-GCN
  [CVPR 2020] L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
  ☆31Updated 3 years ago