Segmentation of polyps based on pyramid vision transformers and residual block for real-time endoscopy imaging

Roi Nachmani, Issa Nidal, Dror Robinson, Mustafa Yassin, David Abookasis

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Polyp segmentation is an important task in early identification of colon polyps for prevention of colorectal cancer. Numerous methods of machine learning have been utilized in an attempt to solve this task with varying levels of success. A successful polyp segmentation method which is both accurate and fast could make a huge impact on colonoscopy exams, aiding in real-time detection, as well as enabling faster and cheaper offline analysis. Thus, recent studies have worked to produce networks that are more accurate and faster than the previous generation of networks (e.g., NanoNet). Here, we propose ResPVT architecture for polyp segmentation. This platform uses transformers as a backbone and far surpasses all previous networks not only in accuracy but also with a much higher frame rate which may drastically reduce costs in both real time and offline analysis and enable the widespread application of this technology.

Original languageEnglish
Article number100197
JournalJournal of Pathology Informatics
Volume14
DOIs
StatePublished - Jan 2023

Keywords

  • Colorectal cancer
  • Computer vision
  • Convolutional neural network
  • Deep learning
  • Pyramid vision transformers
  • Semantic segmentation

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