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.
Material type:
TextSeries: Lecture Notes in Computer Science ; 7009Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: XIII, 371 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642243196Subject(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)Additional physical formats: Printed edition:: No titleDDC classification: 006.6 | 006.37 LOC classification: TA1637-1638TA1637-1638Online 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.
E-BOOKS
| Current library | Home library | Call number | Materials specified | URL | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| IMSc Library | IMSc Library | Link to resource | Available | EBK9934 |
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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