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 in medical imaging1...

(1998 :)

Machine learning in medical imaging11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning in medical imagingedited by Mingxia Liu ... [et al.].
其他題名:	11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /
其他題名:	MLMI 2020
其他作者:	Liu, Mingxia.
團體作者:
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xv, 686 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learningCongresses.
電子資源:	https://doi.org/10.1007/978-3-030-59861-7
ISBN:	9783030598617$q(electronic bk.)

Machine learning in medical imaging11th 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 ;4891..

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$q(electronic bk.)

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

384498
Machine learning
--Congresses.

LC Class. No.: RC78.7.D53

Dewey Class. No.: 006.6

Machine learning in medical imaging11th International Workshop, MLMI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /
LDR:02232nmm a2200373 a 4500 001 585998
003 DE-He213
005 20201002085203.0
006 m d
007 cr nn 008maaau
008 210323s2020 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 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
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 : $c 2020. $b Springer International Publishing : $b Imprint: Springer,
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 $v Congresses. $3 384498
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 445765
650 0 $a Artificial intelligence $x Medical applications $v Congresses. $3 442992
650 1 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Computer Applications. $3 273760
700 1 $a Liu, Mingxia. $3 823091
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/10.1007/978-3-030-59861-7
950 $a Computer Science (SpringerNature-11645)