Yinghuan Shi | Akateeminen Kirjakauppa

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MACHINE LEARNING IN MEDICAL IMAGING - 9TH INTERNATIONAL WORKSHOP, MLMI 2018, HELD IN CONJUNCTION WITH MICCAI 2018, GRANADA, SPAI

Yinghuan Shi; Heung-Il Suk; Mingxia Liu
Springer Nature Switzerland AG (2018)
Saatavuus: Tilaustuote
Pehmeäkantinen kirja

49,60 €

MACHINE LEARNING IN MEDICAL IMAGING - 6TH INTERNATIONAL WORKSHOP, MLMI 2015, HELD IN CONJUNCTION WITH MICCAI 2015, MUNICH, GERMA

Luping Zhou; Li Wang; Qian Wang; Yinghuan Shi
Springer International Publishing AG (2015)
Saatavuus: Tilaustuote
Pehmeäkantinen kirja

49,60 €

MACHINE LEARNING IN MEDICAL IMAGING - 8TH INTERNATIONAL WORKSHOP, MLMI 2017, HELD IN CONJUNCTION WITH MICCAI 2017, QUEBEC CITY,

Qian Wang; Yinghuan Shi; Heung-Il Suk; Kenji Suzuki
Springer International Publishing AG (2017)
Saatavuus: Tilaustuote
Pehmeäkantinen kirja

49,60 €

BIG DATA : 9TH CCF CONFERENCE, BIGDATA 2021, GUANGZHOU, CHINA, JANUARY 8–10, 2022, REVISED SELECTED PAPERS

Xiangke Liao (ed.); Wei Zhao (ed.); Enhong Chen (ed.); Nong Xiao (ed.); Li Wang (ed.); Yang Gao (ed.); Yinghuan Shi (ed.)
Springer (2022)
Saatavuus: Tilaustuote
Pehmeäkantinen kirja

78,60 €

MACHINE LEARNING IN MEDICAL IMAGING - 7TH INTERNATIONAL WORKSHOP, MLMI 2016, HELD IN CONJUNCTION WITH MICCAI 2016, ATHENS, GREEC

Li Wang; Ehsan Adeli; Qian Wang; Yinghuan Shi; Heung-Il Suk
Springer International Publishing AG (2016)
Saatavuus: Tilaustuote
Pehmeäkantinen kirja

49,60 €

Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spai

Yinghuan Shi, Heung-Il Suk, Mingxia Liu

49,60 €

Springer Nature Switzerland AG
Sivumäärä: 409 sivua
Asu: Pehmeäkantinen kirja
Painos: 1st ed. 2018
Julkaisuvuosi: 2018, 15.09.2018 (lisätietoa)
Kieli: Englanti

This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018.The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

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