Toward New-Generation AI-Based CAD System: Development of Interpretable Deep Learning-Based CAD System for Breast Cancer Diagnosis Using Mammogram
| Project/Area Number |
20K08012
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Sendai National College of Technology |
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Principal Investigator |
張 暁勇 仙台高等専門学校, 総合工学科, 准教授 (90722752)
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| Co-Investigator(Kenkyū-buntansha) |
費 仙鳳 東北文化学園大学, 工学部, 准教授 (20620470)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2021: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2020: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | Mammograpy / Deep Learning / Explainable AI / Computer-Aided Diagnosis / Lesion Detection / Interpretablity / Breast Cancer / Computer-Aided Detection / Artificial Intellegence |
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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
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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Report
(4 results)
Research Products
(23 results)
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[Journal Article] Hidden Markov Model-based Extraction of Target Objects in X-ray Image Sequence for Lung Radiation Therapy2020
Author(s)
新藤 雅大, 市地 慶, 本間 経康, 張 曉勇, 奥田 隼梧, 杉田 典大, 八巻 俊輔, 髙井 良尋, 吉澤 誠
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Journal Title
IEEJ Transactions on Electronics, Information and Systems
Volume: 140
Issue: 1
Pages: 49-60
DOI
NAID
ISSN
0385-4221, 1348-8155
Year and Date
2020-01-01
Related Report
Peer Reviewed
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[Presentation] A Deep Learning Aided Drowning Diagnosis for Forensic Investigations Using Post-Mortem Lung CT Images2020
Author(s)
Homma, Noriyasu; Zhang, Xiaoyong; Qureshi, Amber Habib; Konno, Takuya; Kawasumi, Yusuke; Usui, Akihito; Funayama, Masato; Bukovsky, Ivo; Ichiji, Kei; Sugita, Norihiro; Yoshizawa, Makoto
Organizer
42nd Engineering in Medicine and Biology Conference (EMBC 2020)
Related Report
Int'l Joint Research
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