國立虎尾科技大學 |

Machine learning for medical image reconstruction = first International Workshop, MLMIR 2018, held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018 : proceedings /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning for medical image reconstruction/ edited by Florian Knoll, Andreas Maier, Daniel Rueckert.
Reminder of title:	first International Workshop, MLMIR 2018, held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018 : proceedings /
remainder title:	MLMIR 2018
other author:	Knoll, Florian.
corporate name:	Workshop on the Preservation of Stability under Discretization
Published:	Cham :Springer International Publishing : : 2018.,
Description:	x, 158 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Machine learning - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-030-00129-2
ISBN:	9783030001292

Machine learning for medical image reconstruction = first International Workshop, MLMIR 2018, held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018 : proceedings /
Machine learning for medical image reconstructionfirst International Workshop, MLMIR 2018, held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018 : proceedings /[electronic resource] :MLMIR 2018edited by Florian Knoll, Andreas Maier, Daniel Rueckert. - Cham :Springer International Publishing :2018. - x, 158 p. :ill., digital ;24 cm. - Lecture notes in computer science,110740302-9743 ;. - Lecture notes in computer science ;6140..

Deep learning for magnetic resonance imaging -- Deep learning for computed tomography -- Deep learning for general image reconstruction.

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.

ISBN: 9783030001292

Standard No.: 10.1007/978-3-030-00129-2doiSubjects--Topical Terms:

574552
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for medical image reconstruction = first International Workshop, MLMIR 2018, held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018 : proceedings /
LDR:01989nam a2200385 a 4500 001 928973
003 DE-He213
005 20180913021628.0
006 m d
007 cr nn 008maaau
008 190626s2018 sz s 0 eng d
020 $a 9783030001292 $q (electronic bk.)
020 $a 9783030001285 $q (paper)
024 7 $a 10.1007/978-3-030-00129-2 $2 doi
035 $a 978-3-030-00129-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
072 7 $a TJFM1 $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M685 2018
111 2 $a Workshop on the Preservation of Stability under Discretization $d (2001 : $c Fort Collins, Colo.) $3 527686
245 1 0 $a Machine learning for medical image reconstruction $h [electronic resource] : $b first International Workshop, MLMIR 2018, held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018 : proceedings / $c edited by Florian Knoll, Andreas Maier, Daniel Rueckert.
246 3 $a MLMIR 2018
246 3 $a MICCAI 2018
260 $a Cham : $c 2018. $b Springer International Publishing : $b Imprint: Springer,
300 $a x, 158 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11074
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a Deep learning for magnetic resonance imaging -- Deep learning for computed tomography -- Deep learning for general image reconstruction.
520 $a 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.
650 0 $a Machine learning $v Congresses. $3 574552 $3 727808
650 0 $a Artificial intelligence $x Medical applications $v Congresses. $3 675317 $3 742904
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 787070
650 1 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Computer Communication Networks. $3 669310
650 2 4 $a Logic Design. $3 670915
650 2 4 $a Health Informatics. $3 593963
700 1 $a Knoll, Florian. $3 1209222
700 1 $a Maier, Andreas. $3 1069519
700 1 $a Rueckert, Daniel. $3 1076989
710 2 $a SpringerLink (Online service) $3 593884
711 2 $a Workshop on the Preservation of Stability under Discretization $d (2001 : $c Fort Collins, Colo.) $3 527686
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 6140. $3 744199
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 1209216
856 4 0 $u https://doi.org/10.1007/978-3-030-00129-2
950 $a Computer Science (Springer-11645)