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Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer.
Sikkandar, Mohamed Yacin; Sundaram, Sankar Ganesh; Alassaf, Ahmad; AlMohimeed, Ibrahim; Alhussaini, Khalid; Aleid, Adham; Alolayan, Salem Ali; Ramkumar, P; Almutairi, Meshal Khalaf; Begum, S Sabarunisha.
Afiliación
  • Sikkandar MY; Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia. m.sikkandar@mu.edu.sa.
  • Sundaram SG; Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore, 641407, India.
  • Alassaf A; Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
  • AlMohimeed I; Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
  • Alhussaini K; Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, 12372, Saudi Arabia.
  • Aleid A; Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, 12372, Saudi Arabia.
  • Alolayan SA; Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
  • Ramkumar P; Department of Computer Science and Engineering, Sri Sairam College of Engineering, Anekal, Bengaluru, 562106, Karnataka, India.
  • Almutairi MK; Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
  • Begum SS; Department of Biotechnology, P.S.R. Engineering College, Sivakasi, 626140, India.
Sci Rep ; 14(1): 7318, 2024 03 27.
Article en En | MEDLINE | ID: mdl-38538774
ABSTRACT
Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pólipos Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pólipos Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Reino Unido