ShiCrazy

CLAM

Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering

annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

CLAM

Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering

DRAMP-3.0

Scripts for code of DRAMP 3.0

External-Attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

HIPT

HIPT相关代码及学习记录

Pathology-AI-Paper-Notes

AI病理图像论文集笔记

python-wsi-preprocessing

Python Whole Slide Image Preprocessing

ShiCrazy.github.io

vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

ShiCrazy/DRAMP-3.0

Scripts for code of DRAMP 3.0

ShiCrazy/annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

ShiCrazy/CLAM

Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering

ShiCrazy/External-Attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

ShiCrazy/HIPT

HIPT相关代码及学习记录

ShiCrazy/Pathology-AI-Paper-Notes

AI病理图像论文集笔记

ShiCrazy/python-wsi-preprocessing

Python Whole Slide Image Preprocessing

ShiCrazy/ShiCrazy.github.io

ShiCrazy/vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch