Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support  Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings /  [electronic resource] : 
edited by M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu. 
 - 1st ed. 2017. 
 - XIX, 385 p. 169 illus.  online resource. 
 - Image Processing, Computer Vision, Pattern Recognition, and Graphics,  10553  3004-9954 ; .
 - Image Processing, Computer Vision, Pattern Recognition, and Graphics,  10553 .
<br/><br/> This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. 
<br/><br/><label>ISBN: </label>9783319675589 
<br/><br/><label>Standard No.: </label>10.1007/978-3-319-67558-9  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Computer vision.<br/>Artificial intelligence.<br/>Medical informatics.<br/>Bioinformatics.<br/>Logic design.<br/>Computer Vision.<br/>Artificial Intelligence.<br/>Health Informatics.<br/>Computational and Systems Biology.<br/>Logic Design.
<br/><br/><label>LC Class. No.: </label>TA1634 
<br/><br/><label>Dewey Class. No.: </label>006.37