- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Knowledge Graph Search feature is available on CiNii Labs
- 【Updated on June 30, 2025】Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
Cell Image Segmentation by Integrating Generative Adversarial Network for Each Class
-
- TSUDA Hiroki
- Meijo University
-
- HOTTA Kazuhiro
- Meijo University
Bibliographic Information
- Other Title
-
- クラス別敵対的ネットワークの統合による細胞画像のセグメンテーション
Description
<p>Human experts segment cell images manually now, and the criterion for segmentation varies on each expert. As a result, subjective results are obtained. If we develop an automatic segmentation method, we can obtain objective results by the same criteria. This paper proposes a cell image segmentation method using Generative Adversarial Network (GAN) with multiple different roles. The proposed method improved the segmentation accuracy in comparison to conventional pix2pix.</p>
Journal
-
- Proceedings of the Annual Conference of JSAI
-
Proceedings of the Annual Conference of JSAI JSAI2019 (0), 1P4J1003-1P4J1003, 2019
The Japanese Society for Artificial Intelligence
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390845713073508992
-
- NII Article ID
- 130007658316
-
- ISSN
- 27587347
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- CiNii Articles
-
- Abstract License Flag
- Disallowed