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How AI is revolutionizing image analysis


Self-learning algorithms
How AI is revolutionizing image analysis
Image analysis with artificial intelligence (AI) is a major topic of the future in medicine. Medical computer scientists from Heidelberg have now developed a method with which all image data can be processed with AI - free of charge and also usable by laypersons.
Medical informatician Fabian Isensee works in the Medical Image Processing department at the German Cancer Research Center DKFZ. Image: DKFZ.
What can you see on the screen there? Is that part of a lung carcinoma? Or healthy lung tissue? A vessel? A section of a fiber of the heart muscle? Several million times a minute, the algorithm that medical computer scientist Fabian Isensee trained in Klaus Maier-Hein s team at the German Cancer Research Center (DKFZ) asks this question using a new method: What can be seen here? ....

Baden Wuberg , Klaus Maier Hein , Fabian Isensee , Image Processing Department , Imaging Division , German Cancer Research Center , Medical Image Processing , Medical Segmentation , Medical Imaging Division , கிளாஸ் மேயையர் ஹெய்ன் , படம் ப்ரோஸெஸிஂக் துறை , இமேஜிங் பிரிவு , ஜெர்மன் புற்றுநோய் ஆராய்ச்சி மையம் , மருத்துவ படம் ப்ரோஸெஸிஂக் , மருத்துவ இமேஜிங் பிரிவு ,

Children's National Hospital unveils top AI models in COVID-19 Grand Challenge


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The top 10 results have been unveiled in the first-of-its-kind COVID-19 Lung CT Lesion Segmentation Grand Challenge, a groundbreaking research competition focused on developing artificial intelligence (AI) models to help in the visualization and measurement of COVID specific lesions in the lungs of infected patients, potentially facilitating to more timely and patient-specific medical interventions.
Attracting more than 1,000 global participants, the competition was presented by the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children s National Hospital in collaboration with leading AI technology company NVIDIA and the National Institutes of Health (NIH). The competition s AI models utilized a multi-institutional, multi-national data set provided by public datasets from The Cancer Imaging Archive (National Cancer Institute), NIH and the University of Arkansas, that originated from patients of different ages, genders and with variable disease sever ....

United States , Vitali Liauchuk , Ziqi Zhou , Claire Tang , Mona Flores , Fabian Isensee , Bradford Wood , Qinji Yu , Jan Hendrik Moltz , Marius George Linguraru , Andreas Husch , Diana Troese , Bruno Oliveira , Children National , Cancer Imaging Archive National Institute , Lynbrook High School , German Cancer Research Center , Nagoya University , Shenzhen University , United Institute Of Informatics Problems , Northwestern Polytechnical University , National Institutes Of Health , Polytechnic Institute , Fraunhofer Institute For Digital Medicine , University Of Luxembourg , Shanghai Jiao Tong University ,