1 INTRODUCTION  
1.1 History and Scope  
1.2 Related Surveys  
1.3 Our Contributions  

2 METHODOLOGY (SURVEY)  
2.1 Network Architectures  
2.2 Level of Supervision  
2.3 Learning Paradigm  

3 POLYP SEGMENTATION DATASETS  
3.1 Image-level Datasets  
3.2 Video-level Datasets  
3.3 Analysis and Discussion  

4 EVALUATION METRICS  

5 PERFORMANCE BENCHMARKING  
5.1 SOTA Performance Comparisons  
5.2 Out-of-Distribution Generalization  
5.3 Attribute-based Performance Analysis  

6 CHALLENGES AND FUTURE DIRECTIONS  
6.1 Interpretable Deep CPS Model  
6.2 Federated Learning for Data Privacy  
6.3 Domain Adaptation for Domain Shift  
6.4 Defending Against Adversarial Attack  
6.5 Weakly/Un-supervised Learning  
6.6 Lightweight Model for Real-world Application  
6.7 Connecting CPS with Anomaly Detection  
6.8 Combining CPS with Large Segmentation Models  
6.9 Combining CPS with Large Language Models  

7 CONCLUSION