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Automated diagnosis of AD in brain images is becoming a clinically importanttechnique to support precision and efficient diagnosis and treatment planning.A few efforts have been made to automatically diagnose AD in magnetic resonanceimaging (MRI) using three-dimensional CNNs. However, due to the complexity of3D models, the performance is still unsatisfactory, both in terms of accuracyand efficiency. To overcome the complexities of 3D images and 3D models, inthis study, we aim to attack this problem with 2D vision Transformers. Wepropose a 2D transformer-based medical image model with various transformerattention encoders to diagnose AD in 3D MRI images, by cutting the 3D imagesinto multiple 2D slices.The model consists of four main components: sharedencoders across three dimensions, dimension-specific encoders, attention acrossimages from the same dimension, and attention across three dimensions. It isused to obtain attention relationships among multiple sequences from differentdimensions (axial, coronal, and sagittal) and multiple slices. We also proposemorphology augmentation, an erosion and dilation based method to increase thestructural difference between AD and normal images. In this experiment, we usemultiple datasets from ADNI, AIBL, MIRAID, OASIS to show the performance of ourmodel. Our proposed MedTransformer demonstrates a strong ability in diagnosingAD. These results demonstrate the effectiveness of MedTransformer in learningfrom 3D data using a much smaller model and its capability to generalize amongdifferent medical tasks, which provides a possibility to help doctors diagnoseAD in a simpler way.

MedTransformer: Accurate AD Diagnosis for 3D MRI Images through 2D Vision Transformers