| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
The purpose of this research is to propose a visualization knowledge extraction model to overcome the current accuracy limitations in medical data mining. The proposed method automatically cuts out images from medical image data using a computational geometry method. We obtained the result of data knowledge extraction theory construction.
Furthermore, in order to improve the interpretability in machine learning, we have obtained results that show the validity and high quality of the proposed method through theoretical analysis and system implementation experiments on an explanation method that does not depend on the learning model.
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