A python package which aligns histology to the Allen Brain Atlas and Waxholm rat atlas using deep learning.

A pipeline to segment tissue from the background in histological images

SIMPLI is a highly configurable pipeline for the analysis of multiplexed imaging data.

MICCAI 2022: Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images

adaptive color deconvolution for paper "Zheng et al., CMPB, 2019"

The repository contains a simple pipeline for training Nuclei Segmentation Datasets of Histopathology 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

Preprocessing module for large histological images

Code to reproduce results of the Gigapixel Histopathological Image Analysis Using Attention-Based Neural Networks paper.

Segmentation of histological images and fibrosis identification with a convolutional neural network

The nuclei detection method on histology image proposed in the 2017 paper by Peikari et al.

A Point Transformer with Federated Learning for HER2 Status Prediction

Python library designed for tissue microenvironment spatial analysis

Course assignment ~ Classification of breast cancer histology images using Convolutional Neural Networks

Deep Learning breast histology microscopy image recognition using Convolutional Neural Networks

Research Project at ISI ( Indian Statistical Institute ), Kolkata

Solution of Health Data Hack. Task is segmentation of colorectal cancer on high resoultion histological images,

Lightweight image segmentation software for biological images.

Our solution for BreastPathQ cancer cellularity challenge

The Project is an implementation of the paper Blind Color Decomposition of histological image IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 32, NO. 6, JUNE 2013 Milan Gavrilovic*, Member, IEEE, Jimmy C. Azar, Joakim Lindblad, Carolina Wählby, Ewert Bengtsson, Senior Member, IEEE, Christer Busch, and Ingrid B. Carlbom, Member, IEEE