首页|Georgia Institute of Technology & Emory University Reports Finding s in Machine Learning (BiliQML: A supervised machine-learning model to quantify biliary forms from digitized whole-slide liver histopathological images)
Georgia Institute of Technology & Emory University Reports Finding s in Machine Learning (BiliQML: A supervised machine-learning model to quantify biliary forms from digitized whole-slide liver histopathological images)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Atlanta, Georgia, by N ewsRx editors, research stated, “The progress of research focused on cholangiocy tes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dim ensional standard histological cell-counting approaches, which are rapidly perfo rmed error-prone and lack architectural context; or threedimensional analysis o f the biliary tree in opacified livers, which introduce technical issues along w ith minimal quantitation.” Financial supporters for this research include HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases, HHS | NIH | National Institute of D iabetes and Digestive and Kidney Diseases, HHS | NIH | National Institute of Dia betes and Digestive and Kidney Diseases, HHS | NIH | National Institute of Diabe tes and Digestive and Kidney Diseases, Chan Zuckerberg Initiative, HHS | NIH | N ational Institute of Allergy and Infectious Diseases, HHS | NIH | National Insti tute of Allergy and Infectious Diseases, HHS | NIH | National Institute of Aller gy and Infectious Diseases, HHS | NIH | National Institute of Allergy and Infect ious Diseases, HHS | NIH | National Heart, Lung, and Blood Institute, HHS | NIH | National Institute of Biomedical Imaging and Bioengineering, Deutsche Forschun gsgemeinschaft, HHS | NIH | National Institute of Diabetes and Digestive and Kid ney Diseases, HHS | NIH | National Institute of General Medical Sciences.
AtlantaGeorgiaUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine Learning
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