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
Multi-platform, free open source software for visualization and image computing.
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
Dicoogle - Open Source PACS
Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.
Library for multiscale visualization of high-resolution multiplexed bioimaging data on the web. Directly renders Zarr and OME-TIFF.
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
Converts medical images to more displayable formats, e.g. NIfTI to jpg.
Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
Automated 3D brain registration with support for multiple species and atlases.
⚡ Open-source software for deep learning-based digital pathology
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