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Machine Learning in Medical Imaging [electronic resource] :Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers /

Contributor(s): Wang, Fei [editor.] | Shen, Dinggang [editor.] | Yan, Pingkun [editor.] | Suzuki, Kenji [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 7588Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.Description: XII, 276 p. 91 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642354281.Subject(s): Computer science | Database management | Artificial intelligence | Computer graphics | Image processing | Pattern recognition | Computer Science | Image Processing and Computer Vision | Pattern Recognition | Artificial Intelligence (incl. Robotics) | Computer Imaging, Vision, Pattern Recognition and Graphics | Database Management | Computer GraphicsOnline resources: Click here to access online In: Springer eBooksSummary: This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.
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This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.

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