Paper: Deep learning based HEp-2 image classification: A comprehensive review

September 7th, 2020

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.

Fig. 13. Sample categories available in existing HEp-2 public cell image datasets.

S. Rahman, L. Wang, C. Sun, and L. Zhou, Deep learning for HEp-2 image classification: A comprehensive review, Medical Image Analysis, 65:101764, October 2020.

Download theĀ full paper here.

For more information, contact us.

Subscribe to our News via Email

Enter your email address to subscribe and receive notifications of new posts by email.