Code for paper: DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification [ECCV 2024]

Code for the paper " PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers "

[IMAVIS] Official implementation of "ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation".

This repo provides the pipeline of processing TCGA whole-slide images for downstream pathology analysis.

Awesome List of Digital and Computational Pathology Resources

BIBM2023 regular paper for "Addressing Sparse Annotation: a Novel Semantic Energy Loss for Tumor Cell Detection from Histopathologic Images"

A Python package for handling histopathology whole-slide images using multiple instance learning (MIL) techniques.

[MedIA2023 & MICCAI2022 ] Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning

SoftCTM won 3rd place in the OCELOT 2023 Challenge. Multi-organ H&E-based deep learning model for cell detection, applicable for tumor cellularity/ purity/ content estimation.

KBSMC colon cancer grading dataset repository

An open-source UNet-based pipeline for nuclei segmentation in histopathology images using the PanNuke dataset. It features an interactive web app for easy data visualization and handling, making AI tools accessible even for non-experts. This project provides a foundation for training and exploring histopathology data.

The code for LAGE-Net

Implementation of MultiStain-CycleGAN

Package using StarDist and Python that performs object detection and spatial analysis on H&E images

Code for "A Novel Convolution Transformer-Based Network for Histopathology Image Classification Using Adaptive Convolution and Dynamic Attention"

Repository for "Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification""

DL-model for multi-class tissue segmentation in colorectal cancer H&E slides, developed as part of the SemiCOL2023 Challenge.

In this project, we perform exploratory analysis on the data and try out different models to give the best results.

Modifications to the Histomorphological Phenotype Learning pipeline.

Project title: Using Deep Learning to predict overall survival times for breast cancer from H&E whole slide biopsy