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Titlebook: Machine Learning in Medical Imaging; 8th International Wo Qian Wang,Yinghuan Shi,Kenji Suzuki Conference proceedings 2017 Springer Internat

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書目名稱Machine Learning in Medical Imaging
副標(biāo)題8th International Wo
編輯Qian Wang,Yinghuan Shi,Kenji Suzuki
視頻videohttp://file.papertrans.cn/621/620680/620680.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning in Medical Imaging; 8th International Wo Qian Wang,Yinghuan Shi,Kenji Suzuki Conference proceedings 2017 Springer Internat
描述This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. .The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging..
出版日期Conference proceedings 2017
關(guān)鍵詞classification and regression trees; convolutional neural networks; deep learning; factorization method
版次1
doihttps://doi.org/10.1007/978-3-319-67389-9
isbn_softcover978-3-319-67388-2
isbn_ebook978-3-319-67389-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

書目名稱Machine Learning in Medical Imaging影響因子(影響力)




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0302-9743 ical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. .The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad f
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Detection and Localization of Drosophila Egg Chambers in Microscopy Images,ximum likelihood ellipse model fitting. Our proposal is able to detect 96% of human-expert annotated egg chambers at relevant developmental stages with less than 1% false-positive rate, which is adequate for the further analysis.
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Automatic Classification of Proximal Femur Fractures Based on Attention Models,proximal femur fractures. We provide a detailed quantitative and qualitative validation on a dataset of 1000 images and report high accuracy with regard to inter-expert correlation values reported in the literature.
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Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble,h convolutional long short-term memory (CLSTM) to extract voxel labels. Enhanced slice-wise label consistency is ensured, leading to improved segmentation stability and accuracy. We apply our model on ADNI dataset, and demonstrate that our proposed model outperforms the state-of-the-art solutions.
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