Improved Colorectal Polyp Segmentation Using Enhanced MA-NET and Modified Mix-ViT Transformer-Reference-Cited by-同舟云学术

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Improved Colorectal Polyp Segmentation Using Enhanced MA-NET and Modified Mix-ViT Transformer

Published:2023 Issue: Volume:11 Page:69295-69309
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Elkarazle Khaled¹^ORCID,Raman Valliappan²,Then Patrick¹^ORCID,Chua Caslon³

Affiliation:

1. Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, Kuching, Sarawak, Malaysia

2. Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India

3. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC, Australia

Funder

Swinburne University of Technology Sarawak Higher Degree by Research (HDR) Support Fund

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/ielx7/6287639/10005208/10171993.pdf?arnumber=10171993

Reference65 articles.

1. CT colonography: Advanced computer-aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial

2. Vision transformers for remote sensing image classification;bazi;Remote Sens,2021

3. Significance of Missed Polyps at CT Colonography

4. MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

5. Usefulness and limitations of colonoscopy in a proctological clinic

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1. Rethinking encoder-decoder architecture using vision transformer for colorectal polyp and surgical instruments segmentation;Engineering Applications of Artificial Intelligence;2024-10

2. HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management;2024 International Symposium on Medical Robotics (ISMR);2024-06-03

3. Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI;IEEE Access;2024

4. Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches;IEEE Access;2023

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