# DeepPap: Deep Convolutional Networks for Cervical Cell Classification

arXiv: https://arxiv.org/abs/1801.08616 · HTML: https://mlsanity.com/paper/1801.08616/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 23 of 23)

- [Multi-Class Cell Detection Using Spatial Context Representation](https://mlsanity.com/paper/2110.04886/) (2021)
- [DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques](https://mlsanity.com/paper/2102.12191/) (2021)
- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers](https://mlsanity.com/paper/2105.07402/) (2021)
- [Structure Embedded Nucleus Classification for Histopathology Images](https://mlsanity.com/paper/2302.11416/) (2023)
- [Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection](https://mlsanity.com/paper/2106.04919/) (2021)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Cell Graph Transformer for Nuclei Classification](https://mlsanity.com/paper/2402.12946/) (2024)
- [Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review](https://mlsanity.com/paper/2305.03546/) (2023)
- [What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review](https://mlsanity.com/paper/2105.11277/) (2021)
- [Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)](https://mlsanity.com/paper/2112.13553/) (2021)
- [An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Location Deep Features](https://mlsanity.com/paper/2106.15113/) (2021)
- [CytoFM: The first cytology foundation model](https://mlsanity.com/paper/2504.13402/) (2025)
- [Exploring Contextual Relationships for Cervical Abnormal Cell Detection](https://mlsanity.com/paper/2207.04693/) (2022)
- [Geometry-aware Gaussian Prior and Axial Attention for Cervical Cytology Image Classification](https://mlsanity.com/paper/2607.10278/) (2026)
- [A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis](https://mlsanity.com/paper/2204.03572/) (2022)
- [Cervical Glandular Cell Detection from Whole Slide Image with Out-Of-Distribution Data](https://mlsanity.com/paper/2205.14625/) (2022)
- [A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology Classification](https://mlsanity.com/paper/2308.02781/) (2023)
- [Cervical Cancer Detection Using Multi-Branch Deep Learning Model](https://mlsanity.com/paper/2408.10498/) (2024)
- [CerviXpert: A Multi-Structural Convolutional Neural Network for Predicting Cervix Type and Cervical Cell Abnormalities](https://mlsanity.com/paper/2409.06220/) (2024)
- [AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries](https://mlsanity.com/paper/2504.20435/) (2025)
- [Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer](https://mlsanity.com/paper/2505.15505/) (2025)
- [Selective Prediction and Uncertainty-Aware Referral for Pap Smear Classification](https://mlsanity.com/paper/2609.17545/) (2026)

## Abstract (arXiv)

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells - without prior segmentation - based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pre-trained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively re-sampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (AUC) (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross-validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.