SRSegNet: Super-resolution-assisted small targets polyp segmentation network with combined high and low resolution-Reference-Cited by-同舟云学术

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SRSegNet: Super-resolution-assisted small targets polyp segmentation network with combined high and low resolution

Published:2024-03 Issue:3 Volume:36 Page:101981
ISSN:1319-1578
Container-title:Journal of King Saud University - Computer and Information Sciences
language:en
Short-container-title:Journal of King Saud University - Computer and Information Sciences

Author:

Fan Puyin,Diao Yueqin,Li Fan,Zhao Wanlong,Chen Zhu

Funder

National Natural Science Foundation of China

Science and Technology Department of Yunnan Province

Publisher

Elsevier BV

Reference41 articles.

1. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians;Bernal;Comput. Med. Imaging Graph.,2015

2. Polyp-sam++: Can a text guided sam perform better for polyp segmentation?;Biswas,2023

3. A unified multi-scale deep convolutional neural network for fast object detection;Cai,2016

4. DenseUNet+: A novel hybrid segmentation approach based on multi-modality images for brain tumor segmentation;Çetiner;J. King Saud Univ.-Comput. Inf. Sci.,2023

5. Transunet: Transformers make strong encoders for medical image segmentation;Chen,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimized Spatial Transformer for Segmenting Pancreas Abnormalities;Journal of Imaging Informatics in Medicine;2024-09-04

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