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Titlebook: Machine Learning in Medical Imaging; 11th International W Mingxia Liu,Pingkun Yan,Xiaohuan Cao Conference proceedings 2020 Springer Nature

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書目名稱Machine Learning in Medical Imaging
副標題11th International W
編輯Mingxia Liu,Pingkun Yan,Xiaohuan Cao
視頻videohttp://file.papertrans.cn/621/620677/620677.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning in Medical Imaging; 11th International W Mingxia Liu,Pingkun Yan,Xiaohuan Cao Conference proceedings 2020 Springer Nature
描述.This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic...The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc..
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; automatic segmentations; bioinformatics; cellular image analysis; computer visi
版次1
doihttps://doi.org/10.1007/978-3-030-59861-7
isbn_softcover978-3-030-59860-0
isbn_ebook978-3-030-59861-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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A Novel fMRI Representation Learning Framework with GAN,the mapping between mind and brain. The proposed framework is evaluated on Human Connectome Project (HCP) task functional MRI (tfMRI) data. This novel framework proves that GAN can learn meaningful representations of tfMRI and promises better understanding of the brain function.
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3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodimodifications in network architecture, loss function, and data augmentation. As a result, we demonstrate the robustness of our method by automatically segmenting over one hundred distinct bones simultaneously in an end-to-end learnt fashion from a CT-scan.
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Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation,orrespondingly. Finally, a self-contained loss is proposed to supervise labeling process. At experiment section, we conduct comprehensive experiments on collected 526 CCTA scans and exhibit stable and promising results.
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Error Attention Interactive Segmentation of Medical Image Through Matting and Fusion,e automatic segmentation to get higher accuracy for clinical use. Current methods usually transform user clicks to geodesic distance hint maps as guidance, then concatenate them with the raw image and coarse segmentation, and feed them into a refinement network. Such methods are insufficient in refi
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