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wangyh97's Projects

aipy icon aipy

Python机器学习、深度学习算法开发等学习资源分享

attentiondeepmil icon attentiondeepmil

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

awesome-imbalanced-learning icon awesome-imbalanced-learning

Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切:论文、代码、框架与库

clam icon clam

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

deep-histopath icon deep-histopath

A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge

dsmil-wsi icon dsmil-wsi

DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image

imbalanced-dataset-sampler icon imbalanced-dataset-sampler

A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

imgaug icon imgaug

Image augmentation for machine learning experiments.

remix icon remix

[MICCAI2022] This repository holds the Pytorch implementation for the ReMix augmentation described in the paper "ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification"

sish icon sish

Fast and scalable search of whole-slide images via self-supervised deep learning - Nature Biomedical Engineering

smooth-topk icon smooth-topk

Smooth Loss Functions for Deep Top-k Classification

stain-normalizer icon stain-normalizer

A color normalization method for whole slide image(WSI) tiles, coded with cupy and tifffile, accerated by GPU, inspired by torchstain.

tifffile icon tifffile

Read and write TIFF files. Forked from https://pypi.org/project/tifffile

tmi2022 icon tmi2022

A graph-transformer for whole slide image classification

torchstain icon torchstain

Stain normalization tools for histological analysis and computational pathology

tuning_playbook icon tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

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