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Titlebook: Machine Learning for Medical Image Reconstruction; First International Florian Knoll,Andreas Maier,Daniel Rueckert Conference proceedings

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發(fā)表于 2025-3-21 18:42:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Learning for Medical Image Reconstruction
副標(biāo)題First International
編輯Florian Knoll,Andreas Maier,Daniel Rueckert
視頻videohttp://file.papertrans.cn/621/620630/620630.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning for Medical Image Reconstruction; First International  Florian Knoll,Andreas Maier,Daniel Rueckert Conference proceedings
描述.This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018..The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction..
出版日期Conference proceedings 2018
關(guān)鍵詞Artificial Intelligence; Computer architecture; Computerized tomography; Image analysis; Image processin
版次1
doihttps://doi.org/10.1007/978-3-030-00129-2
isbn_softcover978-3-030-00128-5
isbn_ebook978-3-030-00129-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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發(fā)表于 2025-3-21 23:59:02 | 只看該作者
Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstructionsing 25000 images from the UK Biobank dataset we achieve good image quality in the presence of synthetic motion artefacts, but some structural information was lost. We quantitatively compare our method to a standard inverse Fourier reconstruction. In addition, we qualitatively evaluate the proposed
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Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Imageork to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual
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發(fā)表于 2025-3-22 11:39:59 | 只看該作者
Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees landmarks are annotated in supine CBCT reconstructions of the knee joint and then propagated to synthetically generated projection images. Then, a sequential Convolutional Neuronal Network is trained to predict the desired landmarks in projection images. The network is evaluated on synthetic images
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lich gegliederten Stichwortbeschreibungen eine schnelle und dennoch umfassende Information zu allen Themen der Klinischen Chemie (inkl. Gerinnung, H?matologie, Infektionsserologie, Transfusionsmedizi978-3-642-12921-6
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lich gegliederten Stichwortbeschreibungen eine schnelle und dennoch umfassende Information zu allen Themen der Klinischen Chemie (inkl. Gerinnung, H?matologie, Infektionsserologie, Transfusionsmedizi978-3-642-12921-6
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發(fā)表于 2025-3-23 08:49:58 | 只看該作者
Akshay Chaudhari,Zhongnan Fang,Jin Hyung Lee,Garry Gold,Brian Hargreaveslich gegliederten Stichwortbeschreibungen eine schnelle und dennoch umfassende Information zu allen Themen der Klinischen Chemie (inkl. Gerinnung, H?matologie, Infektionsserologie, Transfusionsmedizi978-3-642-12921-6
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