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Paper Abstract and Keywords
Presentation 2020-01-30 13:25
Extracting and Visualization of Essential Features for Staining Translation of Pathological Images
Ryoichi Koga, Noriaki Hashimoto, Tatsuya Yokota (NIT), Masato Nakaguro, Kei Kohno, Shigeo Nakamura (NUI), Ichiro Takeuchi, Hidekata Hontani (NIT) MI2019-116
Abstract (in Japanese) (See Japanese page) 
(in English) In this manuscript, we propose a method for stain translation of pathology images. When one constructs a computer aided diagnosis system that can estimate the subtype of malignant lymphoma from a given H&E stained pathology image, one needs a set of training H&E stained whole-slide pathology images, in which the tumor regions are labeled because each H&E stained image includes both the tumor and non-tumor regions. It is thought not easy to collect enough number of labeled images as the labeling needs large human resources. We hence propose a stain translation method that can convert pathology images in which the tumor regions are stained by some immunostaining to virtual H&E stained images. Once we realize such the stain translation, then we can obtain the training images for training the subtype estimator straightforwardly. Our proposed method extracts image features that contain enough information for translating into any stain images, and a decoder that is specific to each immunostaining generates an virtual image stained with the specific immunostaining from the extracted image features. In addition, we visualize the extracted image features in this manuscript. In the experiments, realized a stain translation from CD20 stained images to H&E stained ones and visualized the corresponding image features.
Keyword (in Japanese) (See Japanese page) 
(in English) pathological image / staining translation / autoencoder / domain adversarial training / natural pre-image / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 399, MI2019-116, pp. 215-218, Jan. 2020.
Paper # MI2019-116 
Date of Issue 2020-01-22 (MI) 
ISSN Online edition: ISSN 2432-6380
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 MI2019-116

Conference Information
Committee MI  
Conference Date 2020-01-29 - 2020-01-30 
Place (in Japanese) (See Japanese page) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2020-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Extracting and Visualization of Essential Features for Staining Translation of Pathological Images 
Sub Title (in English)  
Keyword(1) pathological image  
Keyword(2) staining translation  
Keyword(3) autoencoder  
Keyword(4) domain adversarial training  
Keyword(5) natural pre-image  
1st Author's Name Ryoichi Koga  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Noriaki Hashimoto  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Tatsuya Yokota  
3rd Author's Affiliation Nagoya Institute of Technology (NIT)
4th Author's Name Masato Nakaguro  
4th Author's Affiliation Nagoya University Hospital (NUI)
5th Author's Name Kei Kohno  
5th Author's Affiliation Nagoya University Hospital (NUI)
6th Author's Name Shigeo Nakamura  
6th Author's Affiliation Nagoya University Hospital (NUI)
7th Author's Name Ichiro Takeuchi  
7th Author's Affiliation Nagoya Institute of Technology (NIT)
8th Author's Name Hidekata Hontani  
8th Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2020-01-30 13:25:00 
Presentation Time 45 minutes 
Registration for MI 
Paper # MI2019-116 
Volume (vol) vol.119 
Number (no) no.399 
Page pp.215-218 
Date of Issue 2020-01-22 (MI) 

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