國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine learning for medical image r...

(1998 :)

Machine learning for medical image reconstructionsecond International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for medical image reconstructionedited by Florian Knoll ... [et al.].
其他題名:	second International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
其他題名:	MLMIR 2019
其他作者:	Knoll, Florian.
團體作者:
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	ix, 266 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learningCongresses.
電子資源:	https://doi.org/10.1007/978-3-030-33843-5
ISBN:	9783030338435$q(electronic bk.)

Machine learning for medical image reconstructionsecond International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
Machine learning for medical image reconstructionsecond International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /[electronic resource] :MLMIR 2019edited by Florian Knoll ... [et al.]. - Cham :Springer International Publishing :2019. - ix, 266 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,119050302-9743 ;. - Lecture notes in computer science ;4891..

Deep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction.

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 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.

ISBN: 9783030338435$q(electronic bk.)

Standard No.: 10.1007/978-3-030-33843-5doiSubjects--Topical Terms:

384498
Machine learning
--Congresses.

LC Class. No.: Q325.5 / .M55 2019

Dewey Class. No.: 006.31

Machine learning for medical image reconstructionsecond International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
LDR:04082nmm a2200373 a 4500 001 567968
003 DE-He213
005 20191028192539.0
006 m d
007 cr nn 008maaau
008 200611s2019 sz s 0 eng d
020 $a 9783030338435$q(electronic bk.)
020 $a 9783030338428$q(paper)
024 7 $a 10.1007/978-3-030-33843-5 $2 doi
035 $a 978-3-030-33843-5
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .M55 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M685 2019
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Machine learning for medical image reconstruction $h [electronic resource] : $b second International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings / $c edited by Florian Knoll ... [et al.].
246 3 $a MLMIR 2019
246 3 $a MICCAI 2019
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 266 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11905
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a Deep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction.
520 $a This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 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.
650 0 $a Machine learning $v Congresses. $3 384498
650 0 $a Artificial intelligence $x Medical applications $v Congresses. $3 442992
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 445765
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Computers and Education. $3 274532
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 274376
650 2 4 $a Computational Biology/Bioinformatics. $3 274833
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Health Informatics. $3 274212
700 1 $a Knoll, Florian. $3 823080
710 2 $a SpringerLink (Online service) $3 273601
711 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 823073
856 4 0 $u https://doi.org/10.1007/978-3-030-33843-5
950 $a Computer Science (Springer-11645)