Differentiation between myelodysplastic syndrome and aplastic anemia using transfer learning of vision transformer

We examined the use of a vision transformer (ViT)-based deep learning model for the task of differentiation between aplastic anemia and myelodysplastic syndrome using lumbar T1-weighted images. Three sagittal images per patient were obtained and made square using zero-padding and were resized (224 × 224). The overall accuracy and area under the curve of the pre-trained ViT model were higher than those of ViT without pre-training, ResNet-110, and BinaryNet at the optimum hyperparameters. We utilized Grad-CAM images to highlight the information that is important for decision-making. ViT combined with Grad-CAM successfully recognized variability in the distribution of bone marrow components.