Exploring Polyp Segmentation Through UNet-Based Models with Visual Insight
Author:
Affiliation:
1. Southwest Jiaotong University,School of Computing and Artificial Intelligence,Chengdu,China
2. Chengdu University,Stirling College,Chengdu,P. R. China,610106
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10638789/10638835/10638919.pdf?arnumber=10638919
Reference26 articles.
1. A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation
2. Global colorectal cancer burden in 2020 and projections to 2040
3. Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis
4. PolypSegNet: A modified encoder-decoder architecture for automated polyp segmentation from colonoscopy images
5. ResUNet++: An Advanced Architecture for Medical Image Segmentation
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