Zegami has built a tool that could help speed up diagnosis of COVID-19, using a combination of deep learning and groundbreaking data visualisation. This platform has shown proficiency, using a limited dataset, in distinguishing between x-rays of COVID-19 infections and infections caused by viral or bacterial pneumonia, as well as images of healthy lungs. COVID-19 Xray data was sourced from the Github data initiative, launched by Joseph Paul Cohen. Postdoctoral Fellow, Mila, University of Montreal. It seeks to grow the world’s largest collection of X-ray and CT images of COVID-19 infected lungs to enable automated medical diagnosis.
This mammogram dataset consists of 3486 DICOM (Digital Imaging and Communications in Medicine) images. It contains normal, benign, and malignant cases with verified pathology information.
The Muscle Atlas created for the Institure of Myology, Sorbonne University, contains over a thousand of muscle biopsy images from people of different ages as well as animals.
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