A B S T R A C T  
1. Introduction  
2. Deep Learning Methodology  
  2.1. Supervised Learning  
  2.2. Weakly Supervised Learning  
  2.3. Unsupervised Learning  
  2.4. Transfer Learning  
3. Datasets and Metrics  
4. Deep Learning in Cytology Application  
  4.1. Preprocessing  
  4.2. Classification  
  4.3. Detection  
  4.4. Segmentation  
  4.5. Other Tasks  
5. Challenges and Promises  
  5.1. Label-efficient Learning with Limited Annotations  
  5.2. Fine-grained Classification and Morphological Feature Characterization  
  5.3. Effective Feature Representation Learning  
  5.4. Generalizability and Robustness  
  5.5. Transparency and Interpretability  
  5.6. Digital Medicine and Human-AI Collaboration  
6. Conclusion