Abstract
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.
Publication
Medical Imaging with Deep Learning