SCH: A Multi-Modal Transfer Learning Framework to Maximize Health Outcomes for Breast Canc — NSF Award to University of Nebraska M
Breast cancer is the second most common malignancy and the second leading cause of cancer death among women in the United States. Previous studies indicated that Black women have disproportionately higher mortality rates in breast cancer in the United States. With artificial intelligence (AI) and machine learning (ML)
| Award title | SCH: A Multi-Modal Transfer Learning Framework to Maximize Health Outcomes for Breast Canc |
|---|---|
| Award ID | 2500836 |
| Awardee | University of Nebraska Medical Center |
| City | OMAHA |
| State | NE |
| Amount obligated | $1,000,000 |
| Principal investigator | Shibiao Wan |
| Program | Information Technology Researc, Smart and Connected Health |
| Start date | 08/01/2025 |
| Abstract | Breast cancer is the second most common malignancy and the second leading cause of cancer death among women in the United States. Previous studies indicated that Black women have disproportionately higher mortality rates in breast cancer in the United States. With artificial intelligence (AI) and machine learning (ML) being increasingly applied to cancer research and clinical decision making, especially for breast cancer detection, diagnosis, prognosis and treatment, cancer data variability woul |
| Source | NSF Awards |
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