research-article

A coarse-to-fine segmentation frame for polyp segmentation via deep and classification features

Authors:

Ming LiAuthors Info & Claims

Volume 214, Issue C

https://doi.org/10.1016/j.eswa.2022.118975

Published: 15 March 2023 Publication History

Abstract

Accurate polyp segmentation is of great significance for the diagnosis and treatment of colon cancer. Deep convolution network can extract the common high level features of the target. However, most network models ignore some individual features, so the sample prediction results in complex space are fuzzy and lack of regularity. In this paper, a coarse-to-fine segmentation frame for polyp segmentation via deep and classification features is proposed. Firstly, batch schatten-p norms maximization is introduced into a network model to strengthen the predict map. Then, an automatic two classification mechanism is constructed and the prediction map is classified into two categories: simple and complex samples. Since the CNN prediction maps of simple samples are close to binary images, the prediction maps are not processed. Finally, an active contour model segmentation algorithm for saliency detection of complex samples is proposed. Experiments on Kvasir-SEG, CVC-300, CVC-ClinincDB, CVC-ColonDB and ETIS-LaribPolypDB datasets using multiple models verify the effectiveness of the framework. Code is available at https://doi.org/10.24433/CO.7821162.v1.

Highlights

•

Batch schatten-p norms is introduced into the loss function.

•

An adaptive classification mechanism is constructed.

•

A coarse-to-fine segmentation framework is designed.

•

An ACM based on saliency information is proposed.

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Index Terms

A coarse-to-fine segmentation frame for polyp segmentation via deep and classification features
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 214, Issue C

Mar 2023

1471 pages

ISSN:0957-4174

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 March 2023

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