Transfer Learning With Intelligent Training Data Selection for Prediction of Alzheimer’s Disease


We utilize transfer learning on the ADNI dataset, reduced in sample size by entropy selection to acheive state-of-the-art performance in multi-task classification. We provide a detailed analysis using class activation maps to demonstrate the model’s performance on neuropathological regions that are task-relevant and can help healthcare practicioners in interpreting the model’s decision.

IEEE Access.

A data-driven method to reduce training sample size to mitigate careful model tuning required when using transfer learning for Alzheimer’s disease classification.

In IEEE Access, 2019.