Deep Learning based HEp-2 Image Classification: A Comprehensive Review

TL;DR

This comprehensive review analyzes recent deep learning methods for HEp-2 cell image classification, covering cell and specimen levels, datasets, and future research directions in this challenging field.

Contribution

It systematically organizes existing deep learning approaches for HEp-2 classification, critically analyzes their strengths and weaknesses, and discusses future opportunities.

Findings

Deep learning methods outperform traditional techniques.

Cell-level and specimen-level classification are both effectively addressed.

The review highlights key datasets and future research directions.

Abstract

Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst which deep learning based methods have shown impressive performance. This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods. These methods perform HEp-2 image classification at two levels, namely, cell-level and specimen-level. Both levels are covered in this review. At each level, the methods are organized with a deep network usage based taxonomy. The core idea, notable achievements, and key strengths and weaknesses of each method are critically analyzed. Furthermore, a concise review of the existing HEp-2 datasets that are commonly used in the literature is given. The paper ends with a discussion on novel opportunities and future research directions in this field. It is hoped that this paper would provide readers with a thorough reference of this novel, challenging, and thriving field.