Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support: Second International Workshop, Imimic 2019, and 9th International Workshop, Ml-cds 2019, Held in Conjunction With Miccai 2019, Shenzhen, China, Octobe

Suzuki, Kenji (Editor)/ Reyes, Mauricio (Editor)/ Syeda-Mahmood, Tanveer (Editor)/ Eth Zurich (Corporate Author)/ Glocker, Ben (Editor)

ISBN 10: 3030338495 / ISBN 13: 9783030338497

Published by Springer-Verlag New York Inc, 2019

About this Item

112 pages. 9.25x6.10x0.32 inches. In Stock.

Seller Inventory # x-3030338495

Bibliographic Details

• Title: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support: Second International Workshop, Imimic 2019, and 9th International Workshop, Ml-cds 2019, Held in Conjunction With Miccai 2019, Shenzhen, China, Octobe
• Publisher: Springer-Verlag New York Inc
• Publication Date: 2019
• Language: English
• ISBN 10: 3030338495
• ISBN 13: 9783030338497
• Binding: Paperback
• Condition: Brand New
• Weight: 0.17 kilograms

About this title

Synopsis

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.

The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.