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Paper Abstract and Keywords
Presentation 2021-03-15 15:30
Evaluation of Bayesian Active Learning for Segmentation of Liver and Spleen in Large Scale Abdominal MR Data Sets
Bin Zhang, Yoshito Otake, Mazen Soufi (NAIST), Masatoshi Hori (Kobe University), Noriyuki Tomiyama (Osaka University), Yoshinobu Sato (NAIST) MI2020-60
Abstract (in Japanese) (See Japanese page) 
(in English) Manual annotation in image segmentation is time-consuming and expensive. In order to obtain large number of annotated data set efficiently, Bayesian active learning has been proposed. The key component in the iteration in Bayesian active learning is the selection of query slices (or voxels) which maximize the performance of the model trained in the next iteration. We need to take account for (1) uncertainty estimated from the model trained in the previous iteration, i.e., the distance from the existing training data set, and (2) similarity among the query images. The large batch acquisition with diverse images far from the existing data set enables higher efficiency in active learning. In this study, we investigated the performance and efficiency of several Bayesian active learning approaches specifically for segmentation of liver and spleen in a realistic simulation study using 251 fully annotated abdominal MR data set.
Keyword (in Japanese) (See Japanese page) 
(in English) Bayesian U-net / Bayesian active learning / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 431, MI2020-60, pp. 62-65, March 2021.
Paper # MI2020-60 
Date of Issue 2021-03-08 (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 MI2020-60

Conference Information
Committee MI  
Conference Date 2021-03-15 - 2021-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging 
Paper Information
Registration To MI 
Conference Code 2021-03-MI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of Bayesian Active Learning for Segmentation of Liver and Spleen in Large Scale Abdominal MR Data Sets 
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Keyword(1) Bayesian U-net  
Keyword(2) Bayesian active learning  
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1st Author's Name Bin Zhang  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Yoshito Otake  
2nd Author's Affiliation Nara Institute of Science and Technology (NAIST)
3rd Author's Name Mazen Soufi  
3rd Author's Affiliation Nara Institute of Science and Technology (NAIST)
4th Author's Name Masatoshi Hori  
4th Author's Affiliation Kobe University, Graduate School of Medicine (Kobe University)
5th Author's Name Noriyuki Tomiyama  
5th Author's Affiliation Osaka University, Graduate School of Medicine (Osaka University)
6th Author's Name Yoshinobu Sato  
6th Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2021-03-15 15:30:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2020-60 
Volume (vol) vol.120 
Number (no) no.431 
Page pp.62-65 
#Pages 4 
Date of Issue 2021-03-08 (MI) 

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