| 研究課題/領域番号 |
23K11170
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分61010:知覚情報処理関連
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| 研究機関 | 広島大学 |
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研究代表者 |
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2025年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2024年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
2023年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
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| キーワード | deep learning / automatic image analysis / uncertainty / neural networks / image analysis / reliable AI |
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| 研究開始時の研究の概要 |
In this research project novel deep learning based machine learning methods will be investigated for automatic bio-medical image analysis, which can work reliably in critical settings where safety is first priority.
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| 研究実績の概要 |
In recent years deep neural networks have achieved SOTA(state-of-the-art) performance on many automatic image analysis tasks, even surpassing human-level performance in certain cases. However, they still perform less than satisfactory when test data differs significantly from the data with which they have been trained with (and especially in cases where the training data sets are small). This is particularly important in bio-medical images, where over-confidence in the predictions can lead to life-threatening situations and in images obtained when studying physics-related phenomena, which differ significantly from the natural images used to train the current SOTA deep learning methods.
During the 1st year of the project we have experimented with using Bayesian neural networks to quantify the uncertainty and a baby-step curriculums learning method which mimics how people learn starting from easy data and gradually moving to difficult one. In order to obtain a more objective evaluation of the efficacy of the proposed methods we have decided to use more difficult data: e.g. skin lesions data which is widely used for many medical challenge competitions for medical images, and images of bag-breakup-related fluid phenomena for more general scientific images. In the latter case, we developed a novel algorithm which utilizes information from several consecutive frames and significantly improves accuracy of detection (even compared with present SOTA algorithms) by eliminating false positives which are difficult to discriminate from a single image even for human experts.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The initial plan was to investigate novel machine learning methods (like for example curriculum learning based methods for quantifying and reducing the uncertainty in the automatic analysis of bio-medical images (one concrete example being images of iPS cell colonies, with which we had done some pioneer work before) in order to contribute to the design of more reliable automatic image analysis systems which can work reliably in the presence of uncertainty, especially in cases where it is difficult to obtain a huge number of training images. Obtained experimental results during the first year of the project have shown that better results can be obtained by not limiting our attention to curriculum learning, but for example combining it with novel data augmentation methods (for bio-medical images), or using temporal information from several consecutive frames (in the case of general images of physical phenomena like bag-breakups).
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| 今後の研究の推進方策 |
During the next stage of the project we plan to continue the development of novel deep learning methods for improving the accuracy and reducing the uncertainty in the automatic analysis of both general and bio-medical images, thus endeavoring to contribute to the development of more reliable and trustworthy AI-based systems. We plan to continue our effort for developing a curriculum learning method combined with novel data augmentation methods in the context of bio-medical images and additionally to experiment with some other types of data where uncertainty can lead to life-threatening situations, like for example with data obtained from the cameras of self-driving vehicles.
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