Machine Learning in Medical Imaging [electronic resource] : 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings / edited by Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk.
Contributor(s): Wang, Li [editor.] | Adeli, Ehsan [editor.] | Wang, Qian [editor.] | Shi, Yinghuan [editor.] | Suk, Heung-Il [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 10019Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XIV, 324 p. 127 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319471570.Subject(s): Computer science | Health informatics | Data mining | Artificial intelligence | Image processing | Pattern recognition | Computer Science | Image Processing and Computer Vision | Pattern Recognition | Health Informatics | Data Mining and Knowledge Discovery | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC 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 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.
This book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.
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