Cross-cohort dementia identification using transfer learning with FDG-PET imaging
Machine learning has recently been applied to the identification of dementia syndromes. Accurately labeled PET brain scans used to train machine learning models, however, are difficult to obtain in the clinical environment. Hence we propose a dementia classification method using transfer learning. The main focus of our research is to train a machine learning model using an accurately labeled source image cohort and an unlabeled target image cohort jointly, and then use this model to label the unlabeled target cohort. We show the effectiveness of this knowledge transfer approach by comparing the proposed method to several other methods on public and private image cohorts.