| 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. |
IEICE Tech. Rep. |
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| Conference Information |
| Committee |
ITS IE ITE-MMS ITE-HI ITE-ME ITE-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 |
ITE-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) |
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| 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 |
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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 |
ITE-ME |
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| Volume (vol) |
vol.118 |
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