2022 Fiscal Year Research-status Report
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 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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| Keywords | Mammograpy / Deep Learning / Explainable AI / Computer-Aided Diagnosis / Lesion Detection |
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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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| Causes of Carryover |
Due to the semiconductor shortage, the delivery of GPU-equipped computers for computational purposes is being delayed. As a result, it may not be possible to complete the subsidized project within the designated period.
A GPU device will be installed and remaining experiments will be completed as soon as possibley in the FY2023.
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Research Products
(2 results)
