Pancreatic Tumor Image Segmentation Based on Improved MaxViT
In recent years,deep learning image segmentation techniques have shown promising results in medical image processing.However,segmenting pancreatic tumors in abdominal CT images faces challenging.Due to issues such as varying sizes,fuzzy boundaries,and complex redundant information in pancreatic tumor images,existing networks struggle to achieve precise segmentation.To address this,this study embeds the MaxViT multiscale information extraction attention module into the UNETR network structure to improve upon the deficiencies of the baseline network model.This paper proposes an image segmentation network based on an enhanced UNETR model.
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