This project addresses the problem of accurate detection and class imbalance in Alzheimer’s disease (AD) data by developing a hyperparameter tuning workflow optimized for high-performance computing (HPC).
The project is developed as an educational project for the Politecnico di Milano, for Advanced Methods for Scientific Computing, under the supervision of Professor Paola F. Antonietti, Head of the Laboratory for Modeling and Scientific Computing MOX and Professor of Numerical Analysis at the Politecnico di Milano.
- Zhang, F., Petersen, M., Johnson, L., Hall, J., & O'Bryant, S. E. (2022). Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data. Applied sciences (Basel, Switzerland), 12(13), 6670. https://doi.org/10.3390/app12136670 and https://pmc.ncbi.nlm.nih.gov/articles/PMC9662287/
- Open Access Series of Imaging Studies (OASIS). The Open Access Series of Imaging Studies (OASIS) is a project aimed at making neuroimaging data sets of the brain freely available to the scientific community. By compiling and freely distributing neuroimaging data sets, we hope to facilitate future discoveries in basic and clinical neuroscience.