Machine Learning and Medical Imaging (Elsevier and Micca Society): Guorong Wu, Dinggang Shen, Mert Sabuncu: 9780128040768: Amazon.com: Books

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Guorong Wu is an Assistant Professor of Radiology and Biomedical Research Imaging Center (BRIC) in the University of North Carolina at Chapel Hill. Dr. Wu received his PhD degree from the Department of Computer Science in Shanghai Jiao Tong University in 2007. After graduation, he worked for Pixelworks and joined University of North Carolina at Chapel Hill in 2009. Dr. Wu's research aims to develop computational tools for biomedical imaging analysis and computer assisted diagnosis. He is interested in medical image processing, machine learning and pattern recognition.


Tel Aviv University uses 'Deep Learning' to assist overburdened diagnosticians

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Some 2 billion X-rays are performed around the world every year. But the average radiology clinic is understaffed. Radiologists are burdened with a growing workload, allowing little time to comprehensively evaluate images -- leading to misdiagnoses and more serious consequences. Now a Tel Aviv University lab is engineering practical solutions to meet the demands of radiologists. Prof. Hayit Greenspan's Medical Image Processing Lab in the Department of Biomedical Engineering in the TAU Faculty of Engineering has developed a wide variety of tools to facilitate computer-assisted diagnosis of X-rays, CTs and MRIs, freeing radiologists to attend to complex cases that require their full attention and skills.


Team uses 'Deep Learning' to assist overburdened diagnosticians

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Some 2 billion X-rays are performed around the world every year. But the average radiology clinic is understaffed. Radiologists are burdened with a growing workload, allowing little time to comprehensively evaluate images--leading to misdiagnoses and more serious consequences. Now a Tel Aviv University lab is engineering practical solutions to meet the demands of radiologists. Prof. Hayit Greenspan's Medical Image Processing Lab in the Department of Biomedical Engineering in the TAU Faculty of Engineering has developed a wide variety of tools to facilitate computer-assisted diagnosis of X-rays, CTs and MRIs, freeing radiologists to attend to complex cases that require their full attention and skills.


ICCV19-VRMI

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Dinggang Shen is Jeffrey Houpt Distinguished Investigator, and a Professor in the Department of Radiology and BRIC at UNC-Chapel Hill. His research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1,000 papers in the international journals and conference proceedings. He serves as an editorial board member for eight international journals. He has also served in the Board of Directors of MICCAI Society, in 2012-2015, and will be General Chair for MICCAI 2019.


Yang co-authors book on deep learning and convolutional neural network for biomedical image computing

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This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, microscopic image analysis, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. This book describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database. Dr. Yang is the founder of the Biomedical Image Computing and Imaging Informatics (BICI2) lab (http://www.bme.ufl.edu/labs/yang/). His major research interests are focus on biomedical image analysis and imaging informatics, computer vision, biomedical informatics and machine learning.