Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, ... (Lecture Notes in Computer Science)
- 出版商: Springer
- 出版日期: 2016-09-27
- 售價: $2,380
- 貴賓價: 9.5 折 $2,261
- 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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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)
本書收錄了兩個研討會的審查程序，這兩個研討會是在2016年10月在希臘雅典舉行的第19屆國際醫學影像計算和計算輔助干預會議（MICCAI 2016）上舉辦的。這兩個研討會分別是：第一屆生物醫學數據大規模標註和專家標籤合成研討會（LABELS 2016）和第二屆醫學影像分析深度學習國際研討會（DLMIA 2016）。本書中的28篇修訂過的常規論文經過仔細審查和從總共52篇投稿中選擇出來。其中，LABELS部分選擇了7篇論文，涉及以下領域的主題：群眾外包方法、主動學習、遷移學習、半監督學習和標籤不確定性建模。DLMIA部分選擇了21篇論文，涵蓋了各種主題，如圖像描述、基於醫學影像的診斷、基於醫學信號的診斷、使用深度學習技術進行醫學影像重建和模型選擇、基於深度學習架構的元啟發式技術微調參數以及基於深度學習技術的應用。
