Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First Miccai Workshop, Dart 2019, and Fi
暫譯: 領域適應與表示轉移及醫學影像學習：少量標籤與不完美數據的應用 - 第一次MICCAI研討會，DART 2019及FI
Wang, Qian, Milletari, Fausto, Nguyen, Hien V.
- 出版商: Springer
- 出版日期: 2019-10-12
- 售價: $2,420
- 貴賓價: 9.5 折 $2,299
- 語言: 英文
- 頁數: 254
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3030333906
- ISBN-13: 9783030333904
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商品描述
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.
商品描述(中文翻譯)
本書是2019年第一屆MICCAI領域適應與表示轉移研討會（DART 2019）及第一屆國際醫學影像學習研討會（MIL3ID 2019）的經過審稿的會議論文集，這兩個研討會於2019年10月在中國深圳與MICCAI 2019同時舉行。
DART 2019共接受了18篇投稿中的12篇論文，這些論文探討了方法論的進展和想法，旨在提高機器學習和深度學習方法在臨床環境中的適用性，並使其在不同領域之間保持穩健性和一致性。
MIL3ID共接受了43篇投稿中的16篇論文，這些論文涉及在標籤稀缺和數據不完美的情況下，醫學影像學習的最佳實踐。
