# What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review

arXiv: https://arxiv.org/abs/2105.11277 · HTML: https://mlsanity.com/paper/2105.11277/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 228 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://mlsanity.com/paper/1405.3531/) (2014)
- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [DeepPap: Deep Convolutional Networks for Cervical Cell Classification](https://mlsanity.com/paper/1801.08616/) (2018)
- [IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation](https://mlsanity.com/paper/1908.06623/) (2019)
- [Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks](https://mlsanity.com/paper/1810.06058/) (2018)
- [A Novel Automation-Assisted Cervical Cancer Reading Method Based on Convolutional Neural Network](https://mlsanity.com/paper/1912.06649/) (2019)
- [A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images](https://mlsanity.com/paper/1910.10549/) (2019)
- [PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation](https://mlsanity.com/paper/1911.01062/) (2019)
- [Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model](https://mlsanity.com/paper/1812.00527/) (2018)
- [Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images](https://mlsanity.com/paper/1910.00722/) (2019)
- [Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images](https://mlsanity.com/paper/2002.08331/) (2020)

## Built on by (most cited first, 4 of 4)

- [Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation](https://mlsanity.com/paper/2210.10641/) (2022)
- [A Nasal Cytology Dataset for Object Detection and Deep Learning](https://mlsanity.com/paper/2404.13745/) (2024)
- [A Multi-Stage Optimization Pipeline for Bethesda Cell Detection in Pap Smear Cytology](https://mlsanity.com/paper/2604.13939/) (2026)
- [ReMaD: Tuning-free Domain Adaptation for Classification and Out-of-Distribution Detection](https://mlsanity.com/paper/2610.05718/) (2026)

## Abstract (arXiv)

Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review. We analyzed papers published in the last 5 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.