Medical Xpress: New imaging resource assists AI in the COVID-19 fight

Medical Xpress: New imaging resource assists AI in the COVID-19 fight. “Published today in the Open-Access, Open-Data journal GigaScience is the National COVID-19 Chest Imaging Database (NCCID), a centralized database containing chest X-rays, Computed Tomography (CT) and MRI scans from patients across the UK. Utilizing the unique position as the world’s single largest integrated healthcare system, the benefits of collecting chest imaging data this large are extensive and already being used by doctors and the research community.”

University of Sydney: World-first multiple sclerosis global image database launched

University of Sydney: World-first multiple sclerosis global image database launched. “The new MSBase Imaging Repository (MSBIR) integrates state-of-the-art informatics with an AI analytics engine, fostering a new generation of imaging biomarkers for precision monitoring of MS. It is designed to securely house raw de-identified imaging data for MS patients from multiple sites globally that can be accessed by registered contributing research groups, bringing capacity and scalability to clinical MS imaging research.”

Frederick National Laboratory for Cancer Research: The Cancer Imaging Archive posts COVID-19 imaging data to benefit community

Frederick National Laboratory for Cancer Research: The Cancer Imaging Archive posts COVID-19 imaging data to benefit community. “Publicly available data sets related to COVID-19 are appearing in an unexpected place—the Cancer Imaging Archive (TCIA), a project of the Division of Cancer Treatment and Diagnosis of the National Cancer Institute. Since the start of the pandemic, researchers around the world have been racing to learn as much as possible about the virus—how it spreads, how to diagnose and treat it, and how to develop vaccines against it. One way to help speed up scientific discovery is data sharing.”

Hyde Park Herald: With $20 million in federal funding, U. of C. launches medical imaging database to study COVID-19

Hyde Park Herald: With $20 million in federal funding, U. of C. launches medical imaging database to study COVID-19. “The University of Chicago announced today that it is launching a COVID-19 medical imaging database as part of an initiative to help study the disease using artificial intelligence. The Medical Imaging and Data Resource Center (MIDRC) is funded by a two-year, $20-million federal contract from the National Institutes of Health. Over the next three months, researchers will upload more than 10,000 radiographs and CT-scans of COVID-19 patients to a database.”

Nature: Find a home for every imaging data set

Nature: Find a home for every imaging data set. “Services such as [the Electron Microscopy Public Image Archive] give researchers a central location in which to store, share and access a rapidly expanding corpus of biological images. “The data aren’t just one picture any more,” says Joshua Vogelstein, a neurostatistician at Johns Hopkins University in Baltimore, Maryland. Movies, 3D images and microscope-based screening data can take up gigabytes or terabytes of storage, and can’t be e-mailed back and forth in the same way as individual TIFF or JPEG files. Moreover, grant agencies and journals increasingly require scientists to make their data available to all, but don’t necessarily offer to host them. EMPIAR and its kin fill that gap, and often provide a digital object identifier or other citation so researchers can get credit for their data.”

University at Buffalo: Tool ‘teaches’ computers to correctly annotate medical images

University at Buffalo: Tool ‘teaches’ computers to correctly annotate medical images. “… because machine learning is so complex, medical professionals typically rely on computer engineers to ‘train’ or modify neural networks to properly annotate or interpret medical images. Now, UB researchers have developed a tool that lets medical professionals analyze images without engineering expertise. The tool and the image data that were used for its development are publicly available online.”

Berkeley Lab: Berkeley Lab ‘Minimalist Machine Learning’ Algorithms Analyze Images From Very Little Data

Berkeley Lab: Berkeley Lab ‘Minimalist Machine Learning’ Algorithms Analyze Images From Very Little Data . “Mathematicians at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the tens or hundreds of thousands of images used by typical machine learning methods, this new approach ‘learns’ much more quickly and requires far fewer images.”

Linux Journal: Slicing Scientific Data

Linux Journal: Slicing Scientific Data. “I’ve covered scientific software in previous articles that either analyzes image information or actually generates image data for further analysis. In this article, I introduce a tool that you can use to analyze images generated as part of medical diagnostic work. In several diagnostic medical tests, complex three-dimensional images are generated that need to be visualized and analyzed. This is where 3D Slicer steps into the workflow. 3D Slicer is a very powerful tool for dissecting, analyzing and visualizing this type of complex 3D imaging data. It is fully open source, and it’s available not only on Linux, but also on Windows and Mac OS X.”