Yinghuan Shi | Akateeminen Kirjakauppa

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

Luping Zhou (ed.); Li Wang (ed.); Qian Wang (ed.); Yinghuan Shi (ed.)
Springer (2015)
Pehmeäkantinen kirja

51,40 €

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

Yinghuan Shi (ed.); Heung-Il Suk (ed.); Mingxia Liu (ed.)
Springer (2018)
Pehmeäkantinen kirja

51,40 €

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)
Pehmeäkantinen kirja

51,40 €

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)
Pehmeäkantinen kirja

51,40 €

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)
Pehmeäkantinen kirja

81,40 €

Machine Learning in Medical Imaging : 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germa

Luping Zhou (ed.), Li Wang (ed.), Qian Wang (ed.), Yinghuan Shi (ed.)

51,40 €

Springer
Sivumäärä: 341 sivua
Asu: Pehmeäkantinen kirja
Julkaisuvuosi: 2015, 12.10.2015 (lisätietoa)
Kieli: Englanti
Tuotesarja: Image Processing, Computer Vision, Pattern Recognition, and Graphics

This book constitutes the proceedings of the 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015, held in conjunction with MICCAI 2015, in Munich in October 2015.

The 40 full papers presented in this volume were carefully reviewed and selected from 69 submissions. The workshop focuses on major trends and challenges in the area of machine learning in medical imaging and present works aimed to identify new cutting-edge techniques and their use in medical imaging.

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