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Machine Learning for Medical Image Reconstruction : 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / edited by Nandinee Haq, Patricia Johnson, Andreas Maier, Chen Qin, Tobias Würfl, Jaejun Yoo
(Lecture Notes in Computer Science. ISSN:16113349 ; 13587)

データ種別	電子ブック
版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
大きさ	VIII, 157 p. 83 illus., 54 illus. in color : online resource
著者標目	Haq, Nandinee editor Johnson, Patricia editor Maier, Andreas editor Qin, Chen editor Würfl, Tobias editor Yoo, Jaejun editor SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0001369	Computer Scinece R0 2005-6,2022-3				9783031172472

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一般注記	Deep Learning for Magnetic Resonance Imaging -- Rethinking the optimization process for self-supervised model-driven MRI reconstruction -- NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data -- Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations -- High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors -- Metal artifact correction MRI using multi-contrast deep neural networks for diagnosis of degenerative spinal diseases -- Segmentation-Aware MRI Reconstruction -- MRI Reconstruction with Conditional Adversarial Transformers -- Deep Learning for General Image Reconstruction- A Noise-level-aware Framework for PET Image Denoising -- DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction -- Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, and S. Kevin Zhou Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects -- PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction -- Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging -- DPDudoNet: Deep-Prior based Dual-domain Network for Low-dose Computed Tomography Reconstruction -- MTD-GAN: Multi-Task Discriminator based Generative Adversarial Networks for Low-Dose CT Denoising -- Uncertainty-Informed Bayesian PET Image Reconstruction using a Deep Image Prior This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with MICCAI 2022, in September 2022, held in Singapore. The 15 papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction HTTP:URL=https://doi.org/10.1007/978-3-031-17247-2
件　名	LCSH:Artificial intelligence LCSH:Image processing -- Digital techniques 全ての件名で検索 LCSH:Computer vision LCSH:Computers LCSH:Application software FREE:Artificial Intelligence FREE:Computer Imaging, Vision, Pattern Recognition and Graphics FREE:Computing Milieux FREE:Computer and Information Systems Applications
分　類	LCC:Q334-342 LCC:TA347.A78 DC23:006.3
書誌ID	EB00000757
ISBN	9783031172472