Machine Learning in Medical Imaging 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Suk, Heung-Il (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Liu, Mingxia (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Yan, Pingkun (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Lian, Chunfeng (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11861
• Θέματα: Optical data processing.; Artificial intelligence.; Image Processing and Computer Vision.; Artificial Intelligence.
• Φυσική περιγραφή: XVIII, 695 p. 310 illus., 245 illus. in color. online resource.
• ISBN: 9783030326920
• DOI: 10.1007/978-3-030-32692-0

This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. .