東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Machine learning in medical imaging ...

MLMI (Workshop) (2020 :)

Linked to FindBook

Google Book

Amazon

博客來

Machine learning in medical imaging = 11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in medical imaging/ edited by Mingxia Liu ... [et al.].
Reminder of title:	11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /
remainder title:	MLMI 2020
other author:	Liu, Mingxia.
corporate name:	MLMI (Workshop)
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xv, 686 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-030-59861-7
ISBN:	9783030598617

Machine learning in medical imaging = 11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /
Machine learning in medical imaging11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /[electronic resource] :MLMI 2020edited by Mingxia Liu ... [et al.]. - Cham :Springer International Publishing :2020. - xv, 686 p. :ill., digital ;24 cm. - Lecture notes in computer science,124360302-9743 ;. - Lecture notes in computer science ;12436..

This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

ISBN: 9783030598617

Standard No.: 10.1007/978-3-030-59861-7doiSubjects--Topical Terms:

576368
Machine learning
--Congresses.

LC Class. No.: RC78.7.D53

Dewey Class. No.: 006.6

Machine learning in medical imaging = 11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /
LDR:02232nmm a2200373 a 4500 001 2243633
003 DE-He213
005 20201002085203.0
006 m d
007 cr nn 008maaau
008 211207s2020 sz s 0 eng d
020 $a 9783030598617 $q (electronic bk.)
020 $a 9783030598600 $q (paper)
024 7 $a 10.1007/978-3-030-59861-7 $2 doi
035 $a 978-3-030-59861-7
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53
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 006.6 $2 23
090 $a RC78.7.D53 $b M685 2020
111 2 $a MLMI (Workshop) $n (11th : $d 2020 : $c Lima, Peru) $3 3503865
245 1 0 $a Machine learning in medical imaging $h [electronic resource] : $b 11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings / $c edited by Mingxia Liu ... [et al.].
246 3 $a MLMI 2020
246 3 $a MICCAI 2020
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xv, 686 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 12436
490 1 $a Image processing, computer vision, pattern recognition, and graphics
520 $a This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
650 0 $a Machine learning $x Congresses. $3 576368
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 893542
650 0 $a Artificial intelligence $x Medical applications $x Congresses. $3 660945
650 1 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Computer Applications. $3 891249
700 1 $a Liu, Mingxia. $3 3503866
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (23rd : $d 2020 : $O nline) $3 3503868
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 12436. $3 3503867
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 3382509
856 4 0 $u https://doi.org/10.1007/978-3-030-59861-7
950 $a Computer Science (SpringerNature-11645)