Colonoscopy, currently the most efficient and recognized colon polyp
detection technology, is necessary for early screening and prevention of
colorectal cancer. However, due to the varying size and complex morphological
features of colonic polyps as well as the indistinct boundary between polyps
and mucosa, accurate segmentation of polyps is still challenging. Deep learning
has become popular for accurate polyp segmentation tasks with excellent
results. However, due to the structure of polyps image and the varying shapes
of polyps, it easy for existing deep learning models to overfitting the current
dataset. As a result, the model may not process unseen colonoscopy data. To
address this, we propose a new State-Of-The-Art model for medical image
segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve
the generalization ability of models. Specifically, our proposed Progressive
Locality Decoder can be adapted to the pyramid Transformer backbone to
emphasize local features and restrict attention dispersion. The SSFormer
achieves statet-of-the-art performance in both learning and generalization
assessment.

本文介绍了一种用于医学图像分割的新型深度学习模型 SSFormer，该模型使用了金字塔 Transformer 编码器来改进模型的泛化能力，提高了模型的学习和推广表现。