malabhri Goto Github PK

benchmark-ssl-pathology

Official repository of Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

clam

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

cox_amil

Code for our BVM workshop submission "Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays"

deep_learning_pca

deepops-slurmjob

Package for starting slurm interactive sessions via your local CLI.

dinov2

Adaption of DINOv2 for computational pathology

explainability-dermatology

This repository contains experiments using different XAI methods and ISIC2020 dataset.

good-features

Official code for the paper "A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification"

histopathology-datasets

Ressources of histopathology datasets

neural-image-compression

Code accompanying the paper Neural Image Compression for Gigapixel Histopathology Image Analysis

pathology-he-auto-augment

H&E tailored Randaugment: automatic data augmentation policy selection for H&E-stained histopathology.

pathology-he-autoaugmetation

Comparative analysis of 4 state-of-the-art automatic augmentation algorithms in H&E stained histopathology.

pathology-hooknet

pathology-streaming-pipeline

Use streaming to train whole-slides images with single image-level labels, by reducing GPU memory requirements with 99%.

pathology-streamingclam

Lightstream implementation of the StreamingCLAM model

pathology-whole-slide-data

A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.

pathology-whole-slide-packer

extracts tissue sections from one or multiple whole slide images and combines them into a single new slide removing excess white space

pydiq

Simple DICOM browser in Python (currently not maintained)

pytorch-multilabel-balanced-sampler

PyTorch sampler that outputs roughly balanced batches with support for multilabel datasets

shiprec

Scalable histopathology image preprocessing and feature extraction

sish

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

skin-data-augmentation

Source code for the paper 'Data Augmentation for Skin Lesion Analysis' — 🏆 Best Paper Award at the ISIC Skin Image Analysis Workshop @ MICCAI 2018

ssl_cr_histo

Official code for "Self-Supervised driven Consistency Training for Annotation Efficient Histopathology Image Analysis" Published in Medical Image Analysis (MedIA) Journal, Oct, 2021.

streamingcnn

To train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory available in current GPUs, this limits the maximum dimensions of the input data. Here we demonstrate a method to train convolutional neural networks while holding only parts of the image in memory.

tiger_vuno_algorithm

Fork of the TIGER challenge winner repository

tma_to_wsi

tripath

Analysis of 3D pathology samples using weakly supervised AI - Cell

uni

Towards a general-purpose foundation model for computational pathology - Nature Medicine

visiomel-melanoma

Winners of the VisioMel Challenge: Predicting Melanoma Relapse competition

vissl

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.