ViT-FRD: A Vision Transformer Model for Cardiac MRI Image Segmentation Based on Feature Recombination Distillation

Cardiac magnetic resonance imaging analysis has been a useful tool in screening patients for heart disease. Early, timely and accurate diagnosis of diseases of the heart series is the key to effective treatment. MRI provides important material for the diagnosis of cardiac diseases. The rise of deep learning has transformed computer-aided diagnostic systems, especially in the field of medical imaging. Existing work on cardiac structure segmentation models based on MRI imaging mainly relies on convolutional neural networks (CNNs), which lack model diversity and limit the prediction performance. This paper introduces Visual Transformer with Feature Recombination and Feature Distillation(ViT-FRD), a novel learning pipeline that combines a visual transformer (ViT) and a CNN through knowledge refinement. The training procedure allows the student model, i.e., ViT, to learn from the teacher model, i.e., CNN, by optimizing distillation losses. Meanwhile, ViT-FRD provides two performance boosters to increase the efficacy and efficiency of training. The proposed method is validated on two cardiac MRI image datasets. The findings demonstrate that ViT-FRD achieves SOTA and outperforms the widely used baseline model.