Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Qian Wang

ISBN 10: 3030333906 / ISBN 13: 9783030333904

Published by Springer, Springer Okt 2019, 2019

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains.MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 272 pp. Englisch.

Seller Inventory # 9783030333904

Bibliographic Details

• Title: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data
• Publisher: Springer, Springer Okt 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 3030333906
• ISBN 13: 9783030333904
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 15D millimeters 235 millimeters width by 155 millimeters height by 15 millimeters depth
• Weight: 417 grams

About this title

Synopsis

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains.

MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.