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Paper Abstract and Keywords
Presentation 2024-03-04 13:28
[Short Paper] Experimental validation of relationship between training data amount and accuracy of automatic annotation in developing image diagnosis AI
Koki Muranaka, Mitsutaka Nemoto, Yuichi Kimura, Takashi Nagaoka, Katsuhiro Mikami, Yukako Nakamae (Kindai Univ.), Takeharu Yoshikawa (Tokyo Univ.) MI2023-77
Abstract (in Japanese) (See Japanese page) 
(in English) To reduce the development cost of image diagnosis AI systems, we have studied and proposed an automatic process for creating strongly-supervised lesion region annotation data from weakly-supervised data such as the center coordinates and long diameter of a lesion (Muranaka et al., Japanese Society for Medical and Biological Engineering, 2023). This automatic annotation data generation process applies a region-extraction deep model to a partial region of an image based on the weakly-supervised information, but a certain amount of image data with lesion region annotations is also required for training this deep model. Without a small amount of training data for deep models, development costs cannot be truly reduced. Therefore, in this study, we experimentally verify the relationship between the size of the training data used in our automatic lesion region annotation data generation process and the accuracy of the automatic annotation, and report the results.
Keyword (in Japanese) (See Japanese page) 
(in English) Auto annotation / Deep Learning / U-Net / Region extraction / CT / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 411, MI2023-77, pp. 149-151, March 2024.
Paper # MI2023-77 
Date of Issue 2024-02-25 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2023-77

Conference Information
Committee MI  
Conference Date 2024-03-03 - 2024-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging, etc. 
Paper Information
Registration To MI 
Conference Code 2024-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Experimental validation of relationship between training data amount and accuracy of automatic annotation in developing image diagnosis AI 
Sub Title (in English)  
Keyword(1) Auto annotation  
Keyword(2) Deep Learning  
Keyword(3) U-Net  
Keyword(4) Region extraction  
Keyword(5) CT  
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Keyword(7)  
Keyword(8)  
1st Author's Name Koki Muranaka  
1st Author's Affiliation Kindai University (Kindai Univ.)
2nd Author's Name Mitsutaka Nemoto  
2nd Author's Affiliation Kindai University (Kindai Univ.)
3rd Author's Name Yuichi Kimura  
3rd Author's Affiliation Kindai University (Kindai Univ.)
4th Author's Name Takashi Nagaoka  
4th Author's Affiliation Kindai University (Kindai Univ.)
5th Author's Name Katsuhiro Mikami  
5th Author's Affiliation Kindai University (Kindai Univ.)
6th Author's Name Yukako Nakamae  
6th Author's Affiliation Kindai University (Kindai Univ.)
7th Author's Name Takeharu Yoshikawa  
7th Author's Affiliation Tokyo University (Tokyo Univ.)
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Speaker Author-1 
Date Time 2024-03-04 13:28:00 
Presentation Time 12 minutes 
Registration for MI 
Paper # MI2023-77 
Volume (vol) vol.123 
Number (no) no.411 
Page pp.149-151 
#Pages 3 
Date of Issue 2024-02-25 (MI) 

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