Daoqiang Zhang | Akateeminen Kirjakauppa

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GRAPH LEARNING IN MEDICAL IMAGING - FIRST INTERNATIONAL WORKSHOP, GLMI 2019, HELD IN CONJUNCTION WITH MICCAI 2019, SHENZHEN, CHI

Daoqiang Zhang; Luping Zhou; Biao Jie; Mingxia Liu
Springer Nature Switzerland AG (2019)
Pehmeäkantinen kirja

49,60 €

MACHINE LEARNING IN MEDICAL IMAGING - 4TH INTERNATIONAL WORKSHOP, MLMI 2013, HELD IN CONJUNCTION WITH MICCAI 2013, NAGOYA, JAPAN

Guorong Wu; Daoqiang Zhang; Dinggang Shen; Pingkun Yan; Kenji Suzuki; Fei Wang
Springer International Publishing AG (2013)
Pehmeäkantinen kirja

49,60 €

MACHINE LEARNING IN MEDICAL IMAGING - 5TH INTERNATIONAL WORKSHOP, MLMI 2014, HELD IN CONJUNCTION WITH MICCAI 2014, BOSTON, MA, U

Guorong Wu; Daoqiang Zhang; Luping Zhou
Springer International Publishing AG (2014)
Pehmeäkantinen kirja

49,60 €

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING – IDEAL 2016 - 17TH INTERNATIONAL CONFERENCE, YANGZHOU, CHINA, OCTOBER 12–14

Hujun Yin; Yang Gao; Bin Li; Daoqiang Zhang; Ming Yang; Yun Li; Frank Klawonn; Antonio J. Tallón-Ballesteros
Springer International Publishing AG (2016)
Pehmeäkantinen kirja

49,60 €

HUMAN BRAIN AND ARTIFICIAL INTELLIGENCE : FIRST INTERNATIONAL WORKSHOP, HBAI 2019, HELD IN CONJUNCTION WITH IJCAI 2019, MACAO, C

An Zeng (ed.); Dan Pan (ed.); Tianyong Hao (ed.); Daoqiang Zhang (ed.); Yiyu Shi (ed.); Xiaowei Song (ed.)
Springer (2019)
Pehmeäkantinen kirja

49,60 €

Graph Learning in Medical Imaging - First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, Chi

Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu

49,60 €

Springer Nature Switzerland AG
Sivumäärä: 182 sivua
Asu: Pehmeäkantinen kirja
Painos: 1st ed. 2019
Julkaisuvuosi: 2019, 14.11.2019 (lisätietoa)
Kieli: Englanti
Tuotesarja: Image Processing, Computer Vision, Pattern Recognition, and Graphics

This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019.

The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging.

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