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awesome-deep-learning-single-cell-papers

awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.

bigraph4tme

"Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns from Breast Multiplexed Digital Pathology"

bigraphkernel

Bi-level graphs bridged by Graph Kernels for unveiling Prognosis-relevant Tumor Microenvironment Patterns.

comnecrosis

A machine-learning based computational method for automatic estimation of necrosis ratio in pathology images of osteosarcoma

computer-vison

This is a repository to introduce classic technicals in computer vision and provide related demo. The insights and knowledge come from CS664 Sp2020(Johns Hopkins University).

deepresearch

This repository is the collection of research papers in Deep learning, computer vision and NLP.

dgcnn

A PyTorch implementation of DGCNN based on AAAI 2018 paper "An End-to-End Deep Learning Architecture for Graph Classification"

diffpool

digital-immune

ds4bmetest

ds4ph-bme

A github repo for the Data Science in Public Health and Biomedical Engineering Course

facial-recognition-via-sparse-representation

Project realized as part of a course of Compressed Sensing at ENSAE.

genwl

A Generalized Weisfeiler-Lehman Graph Kernel

grakel

A scikit-learn compatible library for graph kernels

graphkernels

A package for computing Graph Kernels

histogpt

Generating highly accurate pathology reports from gigapixel whole slide images with HistoGPT

hypoxemia

mibc-predictive-models

This repository includes generated data and codes for paper: 'Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer based on nuclear morphology and tissue architecture'.'

p-wl

A Persistent Weisfeiler–Lehman Procedure for Graph Classification

pytorch_dgcnn

PyTorch implementation of DGCNN

scpathology_publication

The Single-Cell Pathology Landscape of Breast Cancer

sign-language-identification

A project of CS601.475 Machine Learning (JHU) that aims to recognize alphabetic letters in American Sign Language.

sparsity-based-face-recognition

structpool

The code for our ICLR paper: StructPool: Structured Graph Pooling via Conditional Random Fields