SwinE-UNet3+: swin transformer encoder network for medical image segmentation

A SwinE-UNet3+ model is proposed to improve the problem that convolutional neural networks cannot capture long-range feature dependencies due to the limitation of receptive field and is insensitive to contour details in tumor segmentation tasks. Each encoder layer of SwinE-UNet3+ uses two consecutive Swin Transformer blocks to extract features, especially long-range features in images. Patch Merging is used for down-sampling between encoder layers. The decoder uses Conv2DTranspose to perform progressive up-sampling and uses convolution operation to aggregate the decoder information after up-sampling and the encoder information through skip connection. The proposed model evaluates the TipDM Cup rectal cancer dataset and the melanoma dermoscopic image ISIC-2017 dataset. Experimental results show that SwinE-UNet3+ model outperforms UNet, UNet++ and UNet3+ models in Dice coefficient, IOU value and Precision evaluation metric.