  Differential diagnosis of dementia is challenging due to overlapping
symptoms, with structural magnetic resonance imaging (MRI) being the primary
method for diagnosis. Despite the clinical value of computer-aided differential
diagnosis, research has been limited, mainly due to the absence of public
datasets that contain diverse types of dementia. This leaves researchers with
small in-house datasets that are insufficient for training deep neural networks
(DNNs). Self-supervised learning shows promise for utilizing unlabeled MRI
scans in training, but small batch sizes for volumetric brain scans make its
application challenging. To address these issues, we propose Triplet Training
for differential diagnosis with limited target data. It consists of three key
stages: (i) self-supervised pre-training on unlabeled data with Barlow Twins,
(ii) self-distillation on task-related data, and (iii) fine-tuning on the
target dataset. Our approach significantly outperforms traditional training
strategies, achieving a balanced accuracy of 75.6%. We further provide insights
into the training process by visualizing changes in the latent space after each
step. Finally, we validate the robustness of Triplet Training in terms of its
individual components in a comprehensive ablation study. Our code is available
at https://github.com/ai-med/TripletTraining.
