ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation

Identifying polyps is challenging for automatic analysis of endoscopic images in computer-aided clinical support systems. Models based on convolutional networks (CNN), transformers, and their combinations have been proposed to segment polyps with promising results. However, those approaches have lim...

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Bibliographic Details
Published in:IEEE access Vol. 10; p. 1
Main Authors: Duc, Nguyen Thanh, Oanh, Nguyen Thi, Thuy, Nguyen Thi, Triet, Tran Minh, Sang, Dinh Viet
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Benchmarks
Coders
Colon
colonoscopy
Computational modeling
Computer architecture
computer-aided diagnosis
Convolutional neural networks
Datasets
Decoding
deep learning
encoder-decoder network
Encoders-Decoders
Feature extraction
hierarchical multi-scale CNN
Image segmentation
Modelling
Polyp segmentation
Polyps
Representations
Semantic segmentation
Semantics
Structural hierarchy
Support systems
Transformers
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Identifying polyps is challenging for automatic analysis of endoscopic images in computer-aided clinical support systems. Models based on convolutional networks (CNN), transformers, and their combinations have been proposed to segment polyps with promising results. However, those approaches have limitations either in modeling the local appearance of the polyps only or lack of multi-level feature representation for spatial dependency in the decoding process. This paper proposes a novel network, namely ColonFormer, to address these limitations. ColonFormer is an encoder-decoder architecture capable of modeling long-range semantic information at both encoder and decoder branches. The encoder is a lightweight architecture based on transformers for modeling global semantic relations at multi scales. The decoder is a hierarchical network structure designed for learning multi-level features to enrich feature representation. Besides, a refinement module is added with a new skip connection technique to refine the boundary of polyp objects in the global map for accurate segmentation. Extensive experiments have been conducted on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib. Experimental results show that our ColonFormer outperforms other state-of-the-art methods on all benchmark datasets.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3195241