株式会社極東書店トップ ＞ 商品一覧 ＞ Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings. 2018 ed.
商品詳細
Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings. 2018 ed.
・ISBN 978-3-030-02627-1 paper EUR 49.99
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| 著者・編者 | Stoyanov, Danail / Taylor, Zeike / Kia, Seyed Mostafa / Oguz, Ipek / Reyes, Mauricio / Martel, Anne / Maier-Hein, Lena / Marquand, Andre F. / Duchesnay, Edouard / Loefstedt, Tommy (eds.), |
|---|---|
| シリーズ | (Lecture Notes in Computer Science) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2018 |
| ページ数 | 149 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A01-85065＞ |
解説
This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.
The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identifythe main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.