Cassie07

PathOmics

[MICCAI 2023 Oral] The official code of "Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction" (top 9%)

Review_Molecular_profile_prediction_GNN

[The Lancet Digital Health, MICCAI2020 Oral] The official code of "Spatially-aware Graph Neural Networks and Cross-level Molecular Profile Prediction in Colon Cancer Histopathology: A Retrospective Multicentre Cohort Study"

SNOW-Dataset

[Scientific Data] The official code of "A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer"

CLIP

Contrastive Language-Image Pretraining

19FallSemesterHW

HW for different courses

Cassie07.github.io

Deep-attention-model-for-relation-extraction

GeneMutationFromHE

Gene mutation and pathway activity prediction from H&E slides

kexinding.github.io

leetcode1

easy

Leetcode2021

SegLoss

A collection of loss functions for medical image segmentation

Useful_Link

A collection of some useful link

version-save

Helpful-Survey-Data-Centric-Foundation-Models-in-Computational-Healthcare

A survey on data-centric foundation models in healthcare.

AiLearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

Data-Centric-FM-Healthcare

A survey on data-centric foundation models in healthcare.

datasetGAN_release

DeepPATH

Classification of Lung cancer slide images using deep-learning

house-condition-evaluation

source code(Especially image segmentation and network architecture part)

Kexin_Ding.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

Leetcode2022

Leetcode_2020

mypy

Optional static typing for Python

PyTorch-learners-tutorial

PyTorch tutorial for learners

SomeUsefulTips

Some useful tips

Transformer_for_medical_image_analysis

A collection of papers about Transformer in the field of medical image analysis.

Trial-MoMA

TripletCell

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level