| 研究課題/領域番号 |
20K08012
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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52040:放射線科学関連
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| 研究機関 | 仙台高等専門学校 |
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研究代表者 |
張 暁勇 仙台高等専門学校, 総合工学科, 准教授 (90722752)
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| 研究分担者 |
費 仙鳳 東北文化学園大学, 工学部, 准教授 (20620470)
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| 研究期間 (年度) |
2020-04-01 – 2024-03-31
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| 研究課題ステータス |
完了 (2023年度)
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| 配分額 *注記 |
4,290千円 (直接経費: 3,300千円、間接経費: 990千円)
2022年度: 520千円 (直接経費: 400千円、間接経費: 120千円)
2021年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2020年度: 2,470千円 (直接経費: 1,900千円、間接経費: 570千円)
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| キーワード | Mammograpy / Deep Learning / Explainable AI / Computer-Aided Diagnosis / Lesion Detection / Interpretablity / Breast Cancer / Computer-Aided Detection / Artificial Intellegence |
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| 研究開始時の研究の概要 |
Deep learning (DL) has attracted extensive efforts for medical image analysis in recent years, such as breast cancer detection in mammogram.. However, since the DL worked in a “black box” fashion, its reliability became a vital problem for clinical application. For solve this problem, this research will focus on developing an interpretable DL-based computer-aided diagnosis system that can not only detect breast cancer in mammograms (making decision), but also produce a visual interpretation to provide understanding of the decision-making process (interpreting decision).
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| 研究実績の概要 |
The purpose of this research is to develop an interpretable deep learning (DL)-based computer-aided diagnosis (CAD) system for breast cancer diagnosis in mammogram. As the final financial year, we mainly achieved the following results.
(1) A new digital mammogram dataset with clinical annotation was collected in cooperation with medical doctors in Tohoku university hospital. (2) The new deep learning training method, which was proposed last FY, was further improved and tested on the new mammogram dataset. The experimental results demonstrate the effectiveness of our proposed method. (3) The methods developed in the project were also extended to other medical application, such as forensic medicine.(4) Four papers have been published in the related international journals.
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