#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

#other #ards #cad #coronavirus #covid #covid_19 #covid_respirator #electronics #fusion_360 #makair_respirators #medical #nucleo_board #sars_cov_2

https://github.com/makers-for-life/makair

#python #deeplabv3 #image_segmentation #medical_image_segmentation #pspnet #pytorch #realtime_segmentation #retinal_vessel_segmentation #semantic_segmentation #swin_transformer #transformer #vessel_segmentation

MMSegmentation is an open-source semantic segmentation toolbox based on PyTorch, part of the OpenMMLab project. It offers a unified benchmark for various semantic segmentation methods, a modular design allowing easy customization, and support for multiple popular segmentation frameworks like PSPNet and DeepLabV3+. The toolbox is highly efficient and provides detailed tutorials, advanced guides, and support for numerous datasets and backbones. This makes it a powerful tool for researchers and developers to implement and develop new semantic segmentation methods efficiently. By using MMSegmentation, you can leverage its flexibility, extensive features, and community support to enhance your projects in image segmentation tasks.

https://github.com/open-mmlab/mmsegmentation

#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

#php #ehr #emr #fhir #global_health #health #healthcare #hit #international #linux #medical #medical_informatics #medical_information #medical_records #openemr #osx #php #practice_management #proprietary_counterparts #sponsors #windows

OpenEMR is a free, open-source electronic health records (EHR) and medical practice management software that works on many platforms like Windows, Linux, and Mac. It offers features such as patient scheduling, electronic billing, integrated health records, and support for both outpatient and inpatient care. It supports modern standards like FHIR for easy and secure data sharing between healthcare providers. OpenEMR is highly customizable, allowing you to tailor it to your specific needs, and it is ONC certified, ensuring compliance with healthcare regulations. Using OpenEMR can save costs compared to paid EHRs and gives you control over your patient data while benefiting from a supportive community and free resources.

https://github.com/openemr/openemr