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.
Contributor(s): Suzuki, Kenji [editor.]
| Wang, Fei [editor.] | Shen, Dinggang [editor.] | Yan, Pingkun [editor.] | SpringerLink (Online service).
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BookSeries: Image Processing, Computer Vision, Pattern Recognition, and Graphics: 7009Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011Edition: 1st ed. 2011.Description: XIII, 371 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642243196.Subject(s): Computer vision | Pattern recognition systems | Image processing -- Digital techniques | Artificial intelligence | Algorithms | Application software | Computer Vision | Automated Pattern Recognition | Computer Imaging, Vision, Pattern Recognition and Graphics | Artificial Intelligence | Algorithms | Computer and Information Systems ApplicationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.37 Online resources: Click here to access online
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Springer Nature eBookSummary: 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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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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