🔮 My Personal Open Source'rer Profile
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Updated
Jun 1, 2024
Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis, and medical intervention.
🔮 My Personal Open Source'rer Profile
Multi-platform, free open source software for visualization and image computing.
Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.
Model for Identification of Alzheimer's Disease by Brain MRI.
A set of common support code for medical imaging, surgical navigation, and related purposes.
Diploma project
Cornerstone is a set of JavaScript libraries that can be used to build web-based medical imaging applications. It provides a framework to build radiology applications such as the OHIF Viewer.
An open-source CMake-based project that provides macros and associated tools for the easy building of 3D Slicer command line interface (CLI) modules.
OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
Histopathology Atlas contains whole slide images of various diseases
Website for NA-MIC Project Weeks
Kaapana (from the hawaiian word kaʻāpana, meaning “distributor” or “part”) 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.
Surgical Image Guidance and Healthcare Toolkit
3D medical imaging reconstruction software
DICOM Web Viewer: open source zero footprint medical image library.
A platform for end-to-end development of machine learning solutions in biomedical imaging
A generalizable application framework for segmentation, regression, and classification using PyTorch
DICOM Viewer
Framework for the reproducible processing of neuroimaging data with deep learning methods