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

Saimunur Rahman¹, Lei Wang², Changming Sun³

¹VILA, School of Computing and Information Technology, University of Wollongong, NSW 2522, Australia; CSIRO Data61, PO Box 76, Epping, NSW 1710, Australia.

Medical Image Analysis

|August 4, 2020

Summary

This summary is machine-generated.

Deep learning methods significantly advance HEp-2 cell classification for autoimmune disease diagnosis. This review analyzes cell-level and specimen-level deep learning approaches, datasets, and future research directions.

Keywords:

Deep learning HEp-2 Cell image classification Review

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Area of Science:

Medical Imaging
Artificial Intelligence
Immunology

Background:

HEp-2 cell pattern classification is crucial for diagnosing autoimmune diseases via indirect immunofluorescence tests.
Deep learning methods have emerged as powerful tools for automated HEp-2 cell classification.

Purpose of the Study:

To provide a comprehensive review of deep learning-based HEp-2 cell image classification methods.
To analyze methods at both cell-level and specimen-level classifications.
To discuss existing HEp-2 datasets and future research opportunities.

Main Methods:

Review and taxonomy of deep learning methods based on network usage.
Critical analysis of core ideas, achievements, strengths, and weaknesses.
Concise review of commonly used HEp-2 datasets.

Main Results:

Deep learning methods demonstrate impressive performance in HEp-2 cell classification.
Methods are categorized by their application at cell-level and specimen-level.
Analysis highlights key aspects and limitations of various approaches.

Conclusions:

Deep learning offers significant advancements in HEp-2 cell classification for autoimmune disease identification.
A thorough understanding of current methods and datasets is essential for future research.
The field presents novel opportunities and challenges for developing more robust classification systems.