| 研究課題/領域番号 |
20K08012
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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 – 2023-03-31
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| キーワード | Mammograpy / Deep Learning / Explainable AI / Computer-Aided Diagnosis |
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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. According to the research plan, the research achievements in the FY2020 are summarized as follows.
(1) Tow datasets, including mammograms, radiologist annotations, and biopsy-proven diagnosis results, have been collected at Tohoku university hospital and Miyagi cancer society.
(2) Preliminary experiments have been conducted to utilize several state-of-the-art end-to-end DL models, such as faster R-CNN, SSD, to detect/localize the suspicious lesions in mammograms.
(3) Four papers have been published in international conferences.
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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
In the FY2021, the research has been conducted and progressed smoothly according to the research plan.
Biopsy diagnosis result and radiologist diagnosis annotation on the datasets have been collected with cooperation of radiologists in Tohoku university hospital. These data will be used to train, test, and evaluate the DL models in the following research work. In addition, based on our current results, two papers have been submitted to prime international journals, and four papers have been published in international conferences.
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| 今後の研究の推進方策 |
According to the research plan, the main research in FY2022 will be focused on the following three tasks. (1) Diagnosis accuracy of DL models will be evaluated quantitatively using collected datasets. (2) Several state-of-the-art visualization techniques will be implemented for generating explainable diagnosis result, and assessed qualitatively in comparison of radiologist diagnosis. (3) A conclusive paper will be submitted to prime international journal.
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| 次年度使用額が生じた理由 |
Due to the COVID-19 pandemic, the travel expense used for attending the domestic and international conferences was not used during the FY2021. In addition, a GPU shortage came about in the late 2020, purchasing a GPU used for deep learning was pending in the FY2021.
We plan to use these part of expense for journal publication fee and international conference registration fee in the next fiscal year
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