EDO-SANet: Shape-Aware Network with Edge Detection Operator for Polyp Segmentation
Author:
Affiliation:
1. North China Electric Power University, China
2. Northeast Normal University, China
3. Institute of Applied Physics and Computational, China
Funder
Beijing Natural Science Foundation
Key R&D Program of the Scientific Research Department
National Natural Science Foundation of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3663976.3664239
Reference43 articles.
1. Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network
2. Jorge Bernal Francisco Javier Sánchez Glòria Fernández-Esparrach Debora Gil Cristina Rodríguez de Miguel and Fernando Vilariño. 2015. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 43 (2015) 99–111. https://doi.org/10.1016/j.compmedimag.2015.02.007
3. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
4. A Computational Approach to Edge Detection
5. Qi Chang Danish Ahmad J.W. Toth Rebecca Bascom and W.E. Higgins. 2022. ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video. In Medical Imaging. https://doi.org/10.1117/12.2647897
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