Presentation 2023-07-03
[Special Talk] Transition of Medical Imaging
Koichi Ito,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Over the past decade, research in medical image processing has dramatically changed. In particular, feature extraction using convolutional neural networks and image segmentation using U-Net, which were developed in the explosion of deep learning, have had a significant impact on medical image processing research, providing the opportunity to solve many difficult problems. In this talk, I introduce the research of medical image processing that has been conducted by our group before and after deep learning.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) medical image processing / deep learning / CT / MRI / ultrasouind / registration / age estimation / pose estimation
Paper # MI2023-10
Date of Issue 2023-06-26 (MI)

Conference Information
Committee MI
Conference Date 2023/7/3(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Tohoku Univ. Sakura Hall
Topics (in Japanese) (See Japanese page)
Topics (in English) Medical Imaging, etc.
Chair Ryo Haraguchi(Univ. of Hyogo)
Vice Chair Hideaki Haneishi(Chiba Univ.) / Takayuki Kitasaka(Aichi Inst. of Tech.)
Secretary Hideaki Haneishi(Yamaguchi Univ.) / Takayuki Kitasaka(NAIST)
Assistant Takeshi Hara(Gifu Univ.) / Kenichi Morooka(Okayama Univ.)

Paper Information
Registration To Technical Committee on Medical Imaging
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) [Special Talk] Transition of Medical Imaging
Sub Title (in English)
Keyword(1) medical image processing
Keyword(2) deep learning
Keyword(3) CT
Keyword(4) MRI
Keyword(5) ultrasouind
Keyword(6) registration
Keyword(7) age estimation
Keyword(8) pose estimation
1st Author's Name Koichi Ito
1st Author's Affiliation Tohoku University(Tohoku Univ.)
Date 2023-07-03
Paper # MI2023-10
Volume (vol) vol.123
Number (no) MI-96
Page pp.pp.11-11(MI),
#Pages 1
Date of Issue 2023-06-26 (MI)