Brain Tumor Segmentation Based on Improved Swin-UNet

Swin UNet combines the characteristics of Swin Transformer and UNet. UNet is a classic image segmentation network with an encoder decoder structure that can provide contextual information while preserving spatial information. Swin Transformer provides a cross window attention mechanism that allows models to obtain global information on images. The core idea of Swin Transformer is to divide the image into non overlapping blocks, and then introduce a cross window attention mechanism, allowing the model to perform self attention calculations within each window to capture global and local information of the image. Swin UNet is mainly used for medical image segmentation tasks, especially for tumor segmentation in brain MRI images. A new segmentation model is proposed by combining the global information acquisition ability of Swin Transformer and the encoder decoder structure of UNet. Swin-UNet-EpA aims to improve segmentation accuracy and performance. For brain tumor segmentation tasks, the performance evaluation of the model usually uses indicators such as IoU and Dice coefficient to measure the degree of matching between the predicted results of the model and the actual labels.