OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
- nci-itcr
- nci-qin
- quantitative-imaging
- imaging-informatics
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OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
3D medical imaging reconstruction software
Clinical Histopathology Imaging Evaluation Foundation Model
A pure JavaScript medical research image viewer.
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.
Kaapana is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. The name Kaapana comes from the Hawaiian word kaʻāpana, meaning "distributor" or "part".
Azure friendly DICOMweb part 18 .NET server with qido-rs, wado-rs, stow-rs, wado-uri RESTful implementation
Automated 3D cell detection in very large images
PyTorch Connectomics: segmentation toolbox for EM connectomics
A Python interface to access neuroanatomical atlases
Interoperable web-based DICOM slide microscopy viewer and annotation tool
Automated 3D brain registration with support for multiple species and atlases.
ENIGMA HALFpipe is a user-friendly software that facilitates reproducible analysis of fMRI data
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
Minerva Story is an image viewer that is designed specifically to make it easy for non-expert users to interact with complex tissue images.
mrQA: tools for quality assurance in medical imaging datasets, including protocol compliance
a collection of small utlities useful for neuroimaging research