| 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 |
交付 (2022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. On the base of the achivement of FY2021, we achived the following progresses in the FY2022.
(1) Experments for evaluation of DL models in lesion detection has been conducted on four mammogram data sets, which were collected in the previous FY.
(2) A new training method, which utilized the clinicians's pixel-wise anotation and saliency maps to improve the DL model acuuracy, was proposed and tested.
(3) Two papers has been published in the related international journals.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
In the FY2022, the paper publication was progressed smoothly according to the research plan. However, the CAD system development was slightly delayed since the experimental device was unavailable.
(1) Two papers about the DL for medical image analysis have been published in the FY2022. And another paper is still under reviewed currently.
(2) A GPU-equipped computer installation was delayed since the global semiconductor shortage in 2022.
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| 今後の研究の推進方策 |
According to the research plan, the main research in FY2023 will be focused on the following three tasks.
(1) Installing the GPU-equipped computer and complete the remaining experiments.
(2) Evaluating the accuracy of DL models in comparison with clinicians screening and assessing whether the screening accuracy of clinicians can be improved with the AI-aided system.
(3) A conclusive paper will be submitted to prime international journal.
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