| Project/Area Number |
18K19842
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 62:Applied informatics and related fields
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| Research Institution | Osaka University |
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Principal Investigator |
Seno Shigeto 大阪大学, 情報科学研究科, 准教授 (30432462)
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| Co-Investigator(Kenkyū-buntansha) |
間下 以大 大阪大学, サイバーメディアセンター, 准教授 (00467606)
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| Project Period (FY) |
2018-06-29 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2018: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
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| Keywords | バイオイメージインフォマティクス / 強化学習 / 深層学習 |
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| Outline of Final Research Achievements |
With the recent development of microscopy technology, images and movies have been produced every day. The methods and purposes of bio-imaging are diverse, but in the case of analyzing images of moving cells, cell recognition and spatio-temporal tracking are universal tasks for extracting information from the images. Traditionally, these tasks have been handled by human tracking or by algorithms that have been developed respectively.
In this study, we developed a method that uses deep learning and reinforcement learning methods to simulate a moving image from the correct solution and use the data to learn the task of cell tracking.
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| Academic Significance and Societal Importance of the Research Achievements |
細胞の移動軌跡のシミュレーションと敵対的生成ネットワーク(GAN) を用いて、少数の正解付き訓練データから類似した疑似データを自動生成する方法の開発を行った。本研究では細胞動画像を対象とした研究を行ったが，シミュレーションとGANによる仮想動画の生成と、それを介した強化学習によって必要なタスクを獲得することができれば、様々な分野での応用が期待できる。
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