The OHIF Medical Imaging Viewer is for viewing medical images. It can retrieve and load images from most sources and formats; render sets in 2D, 3D, and reconstructed representations; allows for the manipulation, annotation, and serialization of observations; supports internationalization, OpenID Connect, offline use, hotkeys, and many more features.

A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)

The Medical Image Registration ToolKit (MIRTK), the successor of the IRTK, contains common CMake build configuration files, core libraries, and basic command-line tools. Extension packages are hosted by the MIRTK GitHub group at

Official public repository of Gate

DICOM Viewer in Nextcloud

DLTK Model Zoo

Open solution to the Data Science Bowl 2018

Tel-Aviv Deep Learning Boot-camp: 12 Applied Deep Learning Labs

COVID deterioration prediction based on chest X-ray radiographs via MoCo-trained image representations

Awesome GAN for Medical Imaging

SegAN: Semantic Segmentation with Adversarial Learning

JavaScript library to display interactive medical images including but not limited to DICOM

Javascript implementation of DICOM manipulation

MNE-CPP: A Framework for Electrophysiology

Official Pytorch Code of KiU-Net for Image Segmentation - MICCAI 2020 (Oral)

Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"

Pure Rust implementation of the DICOM standard

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"