ConvTransNet: Merging Convolution with Transformer to Enhance Polyp Segmentation
Authors: 
Trong-Hieu Nguyen-Mau, Trong-Vu Hoang, Hai-Dang Nguyen, Minh-Triet TranAuthors Info & Claims
Trong-Hieu Nguyen-Mau, Trong-Vu Hoang, Hai-Dang Nguyen, Minh-Triet TranAuthors Info & ClaimsPages 631 - 638
Abstract
Colonoscopy is widely acknowledged as the most efficient screening method for detecting colorectal cancer and its early stages, such as polyps. However, the procedure faces challenges with high miss rates due to the heterogeneity of polyps and the dependence on individual observers. Therefore, several deep learning systems have been proposed considering the criticality of polyp detection and segmentation in clinical practices. While existing approaches have shown advancements in their results, they still possess important limitations. Convolutional Neural Network - based methods have a restricted ability to leverage long-range semantic dependencies. On the other hand, transformer-based methods struggle to learn the local relationships among pixels. To address this issue, we introduce ConvTransNet, a novel deep neural network that combines the hierarchical representation of vision transformers with comprehensive features extracted from a convolutional backbone. In particular, we leverage the features extracted from two powerful backbones, ConvNeXt as CNN-based and Dual Attention Vision Transformer as transformer-based. By incorporating multi-stage features through residual blocks, ConvTransNet effectively captures both global and local relationships within the image. Through extensive experiments, ConvTransNet demonstrates impressive performance on the Kvasir-SEG dataset by achieving a Dice coefficient of 0.928 and an IOU score of 0.882. Additionally, when compared to previous methods on various datasets, ConvTransNet consistently achieves competitive results, showcasing its effectiveness and potential.
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- ConvTransNet: Merging Convolution with Transformer to Enhance Polyp Segmentation
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December 2023
1058 pages
ISBN:9798400708916
DOI:10.1145/3628797
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Published: 07 December 2023
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SOICT 2023
SOICT 2023: The 12th International Symposium on Information and Communication Technology
December 7 - 8, 2023
Ho Chi Minh, Vietnam
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