histopathology

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

Tools for computational pathology

cGAN-based Multi Organ Nuclei Segmentation

Fusing Histology and Genomics via Deep Learning - IEEE TMI

AI-based pathology predicts origins for cancers of unknown primary - Nature

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

Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024

Corresponding code of 'Quiros A.C., Murray-Smith R., Yuan K. Pathology GAN: Learning deep representations of cancer tissue. Proceedings of The 3rd International Conference on Medical Imaging with Deep Learning (MIDL) 2020'

Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021

A pipeline to segment tissue from the background in histological images

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

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)

Implementation of Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification approach.

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

Pytorch stain normalization tools for histological analysis and computational pathology

Fast and Scalable Image Search for Histology

Simple library for preprocessing histopathological whole-slide images (WSI) into tiles (a.k.a. patches) towards deep learning

Whole Slide Image (WSI) Processing Library for Histopathological / Cytopathological Machine Learning Tasks

Dataset of 9536 H&E-stained patches for colorectal polyps classification and adenomas grading | ICIP21 https://doi.org/10.1109/ICIP42928.2021.9506198

REMEX Lab's home page.

Corresponding code of 'Quiros, A. C., Coudray, N., Yeaton, A., Sunhem, W., Murray-Smith, R., Tsirigos, A., Yuan, K. Adversarial learning of cancer tissue representations. The 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021'

WSISR: Single image super-resolution for Whole slide Imaging using convolutional neural networks and self-supervised color normalization.

Repository for the publication "Deep learning enables pathologist-like scoring of NASH models"

Corresponding code of 'Quiros A.C., Murray-Smith R., Yuan K. Learning a low dimensional manifold of real cancer tissue with PathologyGAN 2020'.

Repository for Kubach et al. bioRxiv/2019/804682 (2019)

Image filters for digital pathology: detect pen marks, background, and artifacts. Use them for preprocessing towards deep learning

Kaggle's Histopathologic Cancer Detector

A deep metric learning approach for histopathological image retrieval

A virtual slide viewer (virtual microscope) for extremely high-resolution histopathology images. Made with Python, Flask and Leaflet.