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Paper Abstract and Keywords
Presentation 2019-02-20 13:15
A Note on Gastritis Detection from Gastric X-ray Images via Transfer Learning Approach
Misaki Kanai, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a method for gastritis detection from gastric X-ray images via a transfer learning approach using a convolutional neural network (CNN). CNNs can learn parameters to capture high-dimensional features which express semantic contents by using a large number of labeled images for training and realize accurate image recognition. However, in the field of medical image analysis, lack of the training images often occurs. Concretely, to handle gastric X-ray images used in this paper, it is required to construct a dataset consisting of the images collected from only the specific medical facility since imaging equipment and imaging routine of radiographer are different depending on medical facilities. Therefore, it is difficult to prepare the gastric X-ray images enough to train CNNs in the medical facility which has only a small number of gastric X-ray images. It is reported that fine-tuning, one of the transfer learning approaches, is effective for detection tasks using a small number of the training images. Fine-tuning is a method training a CNN whose parameters are initialized by parameters of a CNN pre-trained with a large number of labeled natural images. Hence, this paper presents a method for gastritis detection from gastric X-ray images which fine-tunes a pre-trained CNN with a small number of gastric X-ray images. Furthermore, this paper shows the effectiveness of the proposed method through experimentation which compares the detection performance of the proposed method with that of a method training a CNN whose parameters are initialized by values randomly sampled from an uniform distribution.
Keyword (in Japanese) (See Japanese page) 
(in English) gastric cancer / gastritis detection / convolutional neural network / transfer learning / gastric X-ray images / / /  
Reference Info. ITE Tech. Rep., vol. 43, no. 5, ME2019-59, pp. 315-318, Feb. 2019.
Paper # ME2019-59 
Date of Issue 2019-02-12 (MMS, HI, ME, AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Committee ME IEICE-IE IEICE-ITS MMS HI AIT  
Conference Date 2019-02-19 - 2019-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ME 
Conference Code 2019-02-ME-IE-ITS-MMS-HI-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Gastritis Detection from Gastric X-ray Images via Transfer Learning Approach 
Sub Title (in English)  
Keyword(1) gastric cancer  
Keyword(2) gastritis detection  
Keyword(3) convolutional neural network  
Keyword(4) transfer learning  
Keyword(5) gastric X-ray images  
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1st Author's Name Misaki Kanai  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Ren Togo  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Takahiro Ogawa  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Miki Haseyama  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2019-02-20 13:15:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # MMS2019-37, HI2019-37, ME2019-59, AIT2019-37 
Volume (vol) vol.43 
Number (no) no.5 
Page pp.315-318 
#Pages
Date of Issue 2019-02-12 (MMS, HI, ME, AIT) 


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