Polyp segmentation is still known as a difficult problem due to the large
variety of polyp shapes, scanning and labeling modalities. This prevents deep
learning model to generalize well on unseen data. However, Transformer-based
approach recently has achieved some remarkable results on performance with the
ability of extracting global context better than CNN-based architecture and yet
lead to better generalization. To leverage this strength of Transformer, we
propose a new model with encoder-decoder architecture named LAPFormer, which
uses a hierarchical Transformer encoder to better extract global feature and
combine with our novel CNN (Convolutional Neural Network) decoder for capturing
local appearance of the polyps. Our proposed decoder contains a progressive
feature fusion module designed for fusing feature from upper scales and lower
scales and enable multi-scale features to be more correlative. Besides, we also
use feature refinement module and feature selection module for processing
feature. We test our model on five popular benchmark datasets for polyp
segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and
ETIS-Larib

本文介绍了一种新型的编码器 - 解码器结构的 LAPFormer 模型，使用分层 Transformer 编码器来提取全局特征，并结合了用于捕获息肉局部外观的 CNN 解码器，包括渐进特征融合模块，特征细化模块和特征选择模块来处理特征，模型在多个息肉分割数据集上进行测试表现良好。