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 |
Granted (Fiscal Year 2022)
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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. 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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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
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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| Strategy for Future Research Activity |
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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Report
(3 results)
Research Products
(15 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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