Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, ... (Lecture Notes in Computer Science)
暫譯: 醫療應用的深度學習與數據標註：第一次國際研討會 LABELS 2016 及第二次國際研討會 DLMIA 2016 ...（計算機科學講義）
- 出版商: Springer
- 出版日期: 2016-09-27
- 售價: $2,420
- 貴賓價: 9.5 折 $2,299
- 語言: 英文
- 頁數: 296
- 裝訂: Paperback
- ISBN: 3319469754
- ISBN-13: 9783319469751
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相關分類:
DeepLearning、Computer-Science
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相關主題
商品描述
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.
The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
商品描述(中文翻譯)
本書為2016年10月在希臘雅典舉行的第19屆國際醫學影像計算與電腦輔助介入會議（MICCAI 2016）中兩個研討會的經過審核的會議論文集：第一次生物醫學數據的大規模標註與專家標籤合成研討會（LABELS 2016）和第二屆醫學影像分析中的深度學習國際研討會（DLMIA 2016）。本書中呈現的28篇修訂過的常規論文是從總共52篇投稿中仔細審核和選出的。選入LABELS的7篇論文涉及以下領域的主題：眾包方法；主動學習；轉移學習；半監督學習；以及標籤不確定性的建模。
選入DLMIA的21篇論文涵蓋了廣泛的主題，例如影像描述；基於醫學影像的診斷；基於醫學信號的診斷；使用深度學習技術的醫學影像重建和模型選擇；用於深度學習架構中參數微調的元啟發式技術；以及基於深度學習技術的應用。
