Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.

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

Image data augmentation on-the-fly by add new class on transforms in PyTorch and torchvision.

HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images (ICCV 2019)

Ressources of histopathology datasets

MICCAI2020. "Multiple Instance Learning with Center Embeddings for Histopathology Image Classification"

1st to MICCAI DigestPath2019 challenge (https://digestpath2019.grand-challenge.org/Home/) on colonoscopy tissue segmentation and classification task. (MICCAI 2019) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

Hard Sample Aware Noise Robust Learning forHistopathology Image Classification

PyTorch implementation of Foveation for Segmentation of Ultra-High Resolution Images

Image classification on lung and colon cancer histopathological images through Capsule Networks or CapsNets.

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

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

Dataset Link

The code for Kernel attention transformer (KAT)

The code for LAGE-Net

🌟 GPU-accelerated stain normalization command line tool

Patoloji Atlası

The aim is to create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. We detect cancer by identifying metastatic tissue in histopathologic scans of lymph nodes using Deep Learning. Implemented using Keras.

Curation of resources of McMedHacks Workshop

Implementation of "Synthetic Data Generation and Classification of Histopathological Images"