# HEp-2 Cell Image Classification with Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1504.02531 · HTML: https://mlsanity.com/paper/1504.02531/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2015.

## Built on by (most cited first, 5 of 5)

- [Self-Paced Learning for Images of Antinuclear Antibodies](https://mlsanity.com/paper/2511.21519/) (2025)
- [Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection](https://mlsanity.com/paper/2210.05175/) (2022)
- [Ensembling Handcrafted Features with Deep Features: An Analytical Study for Classification of Routine Colon Cancer Histopathological Nuclei Images](https://mlsanity.com/paper/2202.10694/) (2022)
- [Maximum margin learning of t-SPNs for cell classification with filtered input](https://mlsanity.com/paper/2303.09065/) (2023)
- [Medical Image Analysis using Deep Relational Learning](https://mlsanity.com/paper/2303.16099/) (2023)

## Abstract (arXiv)

Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper presents an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. This paper elaborates the important components of this framework, discusses multiple key factors that impact the efficiency of training a deep CNN, and systematically compares this framework with the well-established image classification models in the literature. Experiments on benchmark datasets show that i) the proposed framework can effectively outperform existing models by properly applying data augmentation; ii) our CNN-based framework demonstrates excellent adaptability across different datasets, which is highly desirable for classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.