Multi-modality images usually exist for diagnosis/prognosis of a disease, such as Alzheimer's Disease (AD), but with different levels of accessibility and accuracy. MRI is used in the standard of care, thus having high accessibility to patients. On the other hand, imaging of pathologic hallmarks of AD such as amyloid-PET and tau-PET has low accessibility due to cost and other practical constraints, even though they are expected to provide higher diagnostic/prognostic accuracy than standard clinical MRI. We proposed Cross-Modality Transfer Learning (CMTL) for accurate diagnosis/prognosis based on standard imaging modality with high accessibility (mod-HA), with a novel training strategy of using not only data of mod-HA but also knowledge transferred from the model based on advanced imaging modality with low accessibility (mod-LA). We applied CMTL to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD using the Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, demonstrating improved performance of the MRI (mod-HA)-based model by leveraging the knowledge transferred from the model based on tau-PET (mod-LA).