Machine Learning in Medical Imaging : 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

Chunfeng Lian

ISBN 10: 3031210131 / ISBN 13: 9783031210136

Published by Springer International Publishing, Springer Nature Switzerland, 2022

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022. The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned 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 # 9783031210136

Bibliographic Details

• Title: Machine Learning in Medical Imaging : 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings
• Publisher: Springer International Publishing, Springer Nature Switzerland
• Publication Date: 2022
• Language: English
• ISBN 10: 3031210131
• ISBN 13: 9783031210136
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 27D millimeters 235 millimeters width by 155 millimeters height by 27 millimeters depth
• Weight: 744 grams

About this title

Synopsis

This book constitutes the proceedings of the 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022.
The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned 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.