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

Heung-Il Suk

ISBN 10: 3030326918 / ISBN 13: 9783030326913

Published by Springer International Publishing, Springer International Publishing, 2019

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030326913

Bibliographic Details

• Title: Machine Learning in Medical Imaging : 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 3030326918
• ISBN 13: 9783030326913
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.