Machine Learning in Medical Imaging [electronic resource] :Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings / edited by Kenji Suzuki, Fei Wang, Dinggang Shen, Pingkun Yan.
by Suzuki, Kenji [editor.]; Wang, Fei [editor.]; Shen, Dinggang [editor.]; Yan, Pingkun [editor.]; SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 7009Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.Description: XIII, 371 p. online resource.ISBN: 9783642243196.Subject(s): Computer science | Computer software | Artificial intelligence | Computer vision | Optical pattern recognition | Computer Science | Image Processing and Computer Vision | Pattern Recognition | Computer Imaging, Vision, Pattern Recognition and Graphics | Artificial Intelligence (incl. Robotics) | Algorithm Analysis and Problem Complexity | Information Systems Applications (incl. Internet)DDC classification: 006.6 | 006.37 Online resources: Click here to access online
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Springer eBooksSummary: This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| TA1637-1638 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | TA1637-1638 (Browse shelf) | Available |
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This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.
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