SAM-DRA-UNet: An Enhanced U-Net Framework Integrating Knowledge Distillation and Transfer Learning for Brain Tumor Segmentation

Brain tumor segmentation is challenged by irregular morphology, scarce annotations, and class imbalance in medical imaging. This study proposes SAM-DRA-UNet, an enhanced U-Net framework integrating knowledge distillation and transfer learning. We first develop the DRA-UNet architecture by augmenting U-Net’s convolutional blocks with a novel depthwise-pointwise reinforced module and multiple residual simple attention modules, which infer 3D attention maps without parameter expansion while preserving baseline network weights. Furthermore, we employ the SAM model as the teacher network and the DRA-UNet as the student network, transferring knowledge through distillation. Experiments demonstrate that the model achieves mIoU scores of 0.8276 on the TCGA-LGG dataset and 0.8479 on the BraTS21 dataset, significantly outperforming the baseline U-Net and existing state-of-the-art methods. The model also exhibits stable performance across diverse datasets and knowledge distillation temperature settings, validating its generalization capability and providing a reliable solution for brain tumor image segmentation.