國立高雄大學圖資館 |

Language: English

Back

Machine learning in clinical neuroim...

(1998 :)

Machine learning in clinical neuroimaging and radiogenomics in neuro-oncologythird International Workshop, MLCN 2020, and second International Workshop, RNO-AI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in clinical neuroimaging and radiogenomics in neuro-oncologyedited by Seyed Mostafa Kia ... [et al.].
Reminder of title:	third International Workshop, MLCN 2020, and second International Workshop, RNO-AI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /
remainder title:	MLCN 2020
other author:	Kia, Seyed Mostafa.
corporate name:
Published:	Cham :Springer International Publishing :2020.
Description:	xviii, 305 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Diagnostic imagingCongresses.Digital techniques
Online resource:	https://doi.org/10.1007/978-3-030-66843-3
ISBN:	9783030668433$q(electronic bk.)

Machine learning in clinical neuroimaging and radiogenomics in neuro-oncologythird International Workshop, MLCN 2020, and second International Workshop, RNO-AI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /
Machine learning in clinical neuroimaging and radiogenomics in neuro-oncologythird International Workshop, MLCN 2020, and second International Workshop, RNO-AI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /[electronic resource] :MLCN 2020edited by Seyed Mostafa Kia ... [et al.]. - Cham :Springer International Publishing :2020. - xviii, 305 p. :ill., digital ;24 cm. - Lecture notes in computer science,124490302-9743 ;. - Lecture notes in computer science ;4891..

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.

ISBN: 9783030668433$q(electronic bk.)

Standard No.: 10.1007/978-3-030-66843-3doiSubjects--Topical Terms:

445235
Diagnostic imaging
--Digital techniques--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning in clinical neuroimaging and radiogenomics in neuro-oncologythird International Workshop, MLCN 2020, and second International Workshop, RNO-AI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /
LDR:02455nmm a2200385 a 4500 001 591441
003 DE-He213
005 20201230163805.0
006 m d
007 cr nn 008maaau
008 210629s2020 sz s 0 eng d
020 $a 9783030668433$q(electronic bk.)
020 $a 9783030668426$q(paper)
024 7 $a 10.1007/978-3-030-66843-3 $2 doi
035 $a 978-3-030-66843-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M685 2020
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Machine learning in clinical neuroimaging and radiogenomics in neuro-oncology $h [electronic resource] : $b third International Workshop, MLCN 2020, and second International Workshop, RNO-AI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings / $c edited by Seyed Mostafa Kia ... [et al.].
246 3 $a MLCN 2020
246 3 $a RNO-AI 2020
246 3 $a MICCAI 2020
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xviii, 305 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 12449
490 1 $a Image processing, computer vision, pattern recognition, and graphics
520 $a This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.
650 0 $a Diagnostic imaging $x Digital techniques $v Congresses. $3 445235
650 0 $a Machine learning $v Congresses. $3 384498
650 0 $a Electronic data processing $x Distributed processing $v Congresses. $3 384493
650 1 4 $a Image Processing and Computer Vision. $3 274051
700 1 $a Kia, Seyed Mostafa. $3 882911
710 2 $a SpringerLink (Online service) $3 273601
711 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
773 0 $t Springer Nature eBook
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/10.1007/978-3-030-66843-3
950 $a Computer Science (SpringerNature-11645)