#javascript #cancer_imaging_research #dicom #image_analysis #imaging_informatics #medical_image_processing #nci_itcr #nci_qin #quantitative_imaging

https://github.com/OHIF/Viewers

#python #deep_learning #healthcare_imaging #medical_image_computing #medical_image_processing #python3 #pytorch

https://github.com/Project-MONAI/MONAI

#typescript #cancer_imaging_research #dicom #dicom_viewer #hacktoberfest #healthcare_imaging #image_analysis #imaging #imaging_informatics #javascript #medical #medical_image_processing #medical_imaging #nci_itcr #nci_qin #quantitative_imaging #reactjs

The OHIF Medical Imaging Viewer is a powerful tool for viewing medical images. It can load images from various sources and formats, and it supports 2D, 3D, and other advanced viewing options like volume rendering and segmentation. The viewer is highly customizable and extendable, allowing you to add new features as needed. It also supports internationalization, offline use, and user access control. The benefit to you is that it provides a flexible and robust platform for medical image analysis, tailored to your specific needs, with a strong community and extensive support resources.

https://github.com/OHIF/Viewers

#python #deep_learning #healthcare_imaging #medical_image_computing #medical_image_processing #monai #python3 #pytorch

MONAI is a free and open-source tool for using deep learning in healthcare imaging. It is based on PyTorch and helps researchers and scientists create and evaluate deep learning models easily. MONAI offers flexible preprocessing for medical images, easy-to-use APIs, and support for multiple GPUs and nodes. This makes it easier to integrate into existing workflows and allows users of different expertise levels to use it. You can install MONAI simply by running `pip install monai` and find tutorials and documentation on their website. Using MONAI can help you develop state-of-the-art healthcare imaging models quickly and efficiently.

https://github.com/Project-MONAI/MONAI