| Project/Area Number |
24K15011
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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 61010:Perceptual information processing-related
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| Research Institution | Nagasaki University |
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Principal Investigator |
Kavitha Muthu・Subash 長崎大学, 総合生産科学研究科(情報データ科学系), 助教 (00909278)
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| Co-Investigator(Kenkyū-buntansha) |
石丸 英樹 長崎大学, 医歯薬学総合研究科(医学系), 准教授 (00625858)
酒井 智弥 長崎大学, 情報データ科学部, 准教授 (30345003)
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| Project Period (FY) |
2024-04-01 – 2027-03-31
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| Project Status |
Granted (Fiscal Year 2024)
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| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2026: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2025: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2024: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | Data Saving / Self Supervised |
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| Outline of Research at the Start |
Ischemic strokes occur when brain blood flow is blocked, causing cell death. Quick, accurate action is vital to minimize damage. Current AI methods require extensive data and manual labeling by doctors. Our new method saves time and data by using parallel computing to spot possible stroke areas without needing as much data. This means it can find stroke sites on its own without manual labeling. We'll focus on two main goals: quickly and affordably diagnosing ischemic stroke using brain PET scans and predicting stroke recurrence risk and brain changes for real-life medical follow-ups.
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