重庆工商大学学报（自然科学版）2024，Vol.41Issue(5)：49-57,9.DOI:10.16055/j.issn.1672-058X.2024.0005.006
基于R-DCAformer的结直肠息肉分割模型
Colorectal Polyp Segmentation Model Based on R-DCAfomer
摘要
Abstract
Objective Although the existing Transformer model has high accuracy in segmenting colorectal polyps with complex morphology,the distraction of the Transformer model and the loss of information in the fusion of its encoder outputting multilevel semantic information limit the further improvement of the model's accuracy.Based on this,a novel image segmentation model(the Dual-Channel Aggregation Transformer,R-DCAformer)for intestinal polyps was proposed.Methods The R-DCAformer model used a pyramid mix Transformer(MIT)and Resnet18 to act as an encoder and a dual-channel aggregation(DCA)module was designed to act as a decoder.The DCA decoder consisted of an attention aggregation(AA)module and a dual-channel feature fusion(DFF)module.In this model,the pyramid MIT encoder provided sufficient generalization ability for the model,the AA module limited the distraction in the model MIT by fusing the additional features of Resnet18,and the DFF module alleviated the problem of information loss in the fusion of multi-level semantic information.Results In the generalization ability experiment,R-DCAformer improved the optimal mDice,mIoU,and MAE by 2.10％,1.65％,and 22.5％,respectively,in CVC-ColonDB compared with the optimal ones in the baseline model.The optimal mDice,mIoU,and MAE in ETIS were improved by 2.56％,2.12％,and 15％,respectively,compared with the optimal ones in the baseline model.The model improved the optimal mDice and mIoU by about 0.85％and 1.35％in the CVC-ClinicDB dataset compared with the optimal ones in the baseline model,and the optimal mDice,mIoU,and MAE on the Kvasir-SEG dataset were improved by about 1.19％,1.97％,and 17.39％,respectively,compared with those in the baseline model.The effectiveness of the module proposed in this paper was also demonstrated by ablation experiments and attention graphs.Conclusion R-DCAformer is more effective in both learning and generalization experiments,and generally outperforms the compared baseline models,providing a new high-performance model for colorectal polyp segmentation.关键词
息肉图像分割/深度学习/双通道聚合/注意力聚合/泛化能力Key words
polyp image segmentation/deep learning/dual-channel aggregation/attention aggregation/generalization ability分类
信息技术与安全科学引用本文复制引用
高艾国,郑晓亮..基于R-DCAformer的结直肠息肉分割模型[J].重庆工商大学学报（自然科学版）,2024,41(5):49-57,9.基金项目
煤炭安全精准开采国家地方联合工程研究中心开放基金资助(EC2021006) （EC2021006）
安徽理工大学高层次引进人才科研启动基金资助(2021YJRC02). （2021YJRC02）
