Multi-scale nuclei segmentation model for breast cancer histopathology using contextual patches, VGG16-based feature extraction, channel attention, and a U-Net decoder. Official Python implementation of the ISBI 2025 paper “M3-Net” with training, evaluation, and visualization workflows.

Merge annotation xml file of Aperio Imagescope.

Official PyTorch Implementation of paper 'Edge-Based Graph Neural Networks for Cell-Graph Modeling and Prediction'

stain augmentation method based on stainmix up using multiple known stain matrices.

This repository contains official code for the WACV 2023 paper "HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks".

(WIP) PyTorch/TensorFlow powered semantic segmentation software for cytological slides

Self-Supervised Learning in Medicine Review

Feature extraction from GEOJson nuclei and tissue segmentation maps

Using Sectra PACS Annotations in QuPath

An implementation of deep learning segmentation models in digital pathology field

Scripts used for the "Glycophorin C in carotid atherosclerotic plaque reflects intraplaque hemorrhage and pre-procedural neurological symptoms" paper.

Med-CrossViT: A Transformer-based architecture for WSI and RNA-Seq data fusion

Official code for Prediction of molecular subtypes for endometrial cancer based on hierarchical foundation model.

Pathology slide → structured diagnostic report generator. Uses CLIP/BLIP embeddings + RAG over medical knowledge to summarize histopathology images.

Deep learning pipeline for nuclei instance segmentation in histopathology using U-Net and MONAI.

Python 3 Package for optimally sampling big images with texture-aware patchification based on SLIC superpixels. So Sleek !

Whole Slide Image Stain Normalisation and Augmentation

PyPathomics is an open-source software for gigapixel whole-slide image analysis. Extract features directly from HoverNet's json file.

Official code repository for MOSAIC: a multi-scale orientation-aware nuclei segmentation and classification network for digital pathology.