Ressources of histopathology datasets

Federated Learning for Computational Pathology - Medical Image Analysis

PyTorch implementation of Learning to Downsample for Segmentation of Ultra-High Resolution Images

HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg

Python package that uses colorspace-based segmentation to analyze histopathology images.

Automated liver NAFLD/NASH scoring

Training a local neural network from multiple MRI to histology

Complete Matlab pipeline for matching in vivo recorded cells post hoc in histology

Pytorch implementation of NEGEV method. Paper: "Negative Evidence Matters in Interpretable Histology Image Classification".

Deep-learning based classification pipeline for subtyping lung tumors from histology. Study design and codebase to analyze the impact of nucleus segmentation on subtyping.

Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data

Gland segmentation for histology images using Tensorflow

Atelier3D wiki

DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions

Using Convolutional Neural Network and transfer learning to create an accurate classification model of Invasive Ductal Carcinoma in Histology Images. Then deploying this model into a simple front end.

Prediction of gene expression patterns from histology images using deep learning derived features

[ISBI 2024] Accurate Subtyping of Lung Cancers by Modelling Class Dependencies

OpenSeadragon histology imaging demos

An automated image analysis tool for quantification of fat cells

GUI application for the Fatquant tool