A Two-Stage Method for Polyp Detection in Colonoscopy Images Based on Saliency Object Extraction and Transformers-Reference-Cited by-同舟云学术

A Two-Stage Method for Polyp Detection in Colonoscopy Images Based on Saliency Object Extraction and Transformers

Published:2023 Issue: Volume:11 Page:76108-76119
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lima Alan Carlos de Moura¹^ORCID,de Paiva Lisle Faray²,Bráz Geraldo¹^ORCID,de Almeida João Dallyson S.¹,Silva Aristófanes Corréa¹^ORCID,Coimbra Miguel Tavares³^ORCID,de Paiva Anselmo Cardoso¹

Affiliation:

1. NCA, Federal University of Maranhão, São Luís, Brazil

2. UFR SC, University of Burgundy, Le Creusot, France

3. INESC TEC, Faculty of Sciences, University of Porto, Porto, Portugal

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil

Fundação de Amparo á Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão (FAPEMA), Brazil

National Funds through the Portuguese funding agency, FCT—Fundacao para a Ciencia e a Tecnologia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10187140.pdf?arnumber=10187140

Reference51 articles.

1. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Proc Int Conf Med Image Comput Comput -Assist Intervent,2015

2. Polyp segmentation in colonoscopy images using U-Net-MobileNetV2;branch;arXiv 2103 15715,2021

3. Aggregated Residual Transformations for Deep Neural Networks

4. MobileNetV2: Inverted Residuals and Linear Bottlenecks

5. A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation

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