mlsanityData to 7 Oct 2026

Deep Learning for Computational Cytology: A Survey

arXiv 2202.05126 ↗First posted 10 Feb 2022eess.IV cs.CV cs.LGcited by 20

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Abstract (arXiv)

Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) algorithms have made significant progress in medical image analysis, leading to the boosting publications of cytological studies. To investigate the advanced methods and comprehensive applications, we survey more than 120 publications of DL-based cytology image analysis in this article. We first introduce various deep learning methods, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize the public datasets, evaluation metrics, versatile cytology image analysis applications including classification, detection, segmentation, and other related tasks. Finally, we discuss current challenges and potential research directions of computational cytology.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 68 of its 235 references that are in the map. First posted on arXiv 10 Feb 2022.

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68 of 235 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 7; 2024: 7; 2025: 2; 2026: 2.

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  1. 2023DoNet: Deep De-overlapping Network for Cytology Instance Segmentationcited by 16
  2. 2023Image Quality-aware Diagnosis via Meta-knowledge Co-embeddingcited by 12
  3. 2023Digital staining in optical microscopy using deep learning -- a reviewcited by 7
  4. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  5. 2024Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathologycited by 4
  6. 2024GAInS: Gradient Anomaly-aware Biomedical Instance Segmentationcited by 2
  7. 2024PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Networkcited by 2
  8. 2022Exploring Contextual Relationships for Cervical Abnormal Cell Detectioncited by 1
  9. 2024Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological imagescited by 1
  10. 2025MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Imagecited by 1
  11. 2022Cervical Glandular Cell Detection from Whole Slide Image with Out-Of-Distribution Datacited by 0
  12. 2023LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screeningcited by 0
8 more
  1. 2023Gravity Network for end-to-end small lesion detectioncited by 0
  2. 2023Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Imagescited by 0
  3. 2024Fuzzy Rank-based Late Fusion Technique for Cytology image Segmentationcited by 0
  4. 2024A Nasal Cytology Dataset for Object Detection and Deep Learningcited by 0
  5. 2024Holistic and Historical Instance Comparison for Cervical Cell Detectioncited by 0
  6. 2025RAA-MIL: A Novel Framework for Classification of Oral Cytologycited by 0
  7. 2026A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categoriescited by 0
  8. 2026Group Equivariant Diffusion for Anomaly Detection in Computational Cytologycited by 0