VILA, School of Computing and Information Technology, University of Wollongong, NSW 2522, Australia; CSIRO Data61, PO Box 76, Epping, NSW 1710, Australia.
VILA, School of Computing and Information Technology, University of Wollongong, NSW 2522, Australia.
Med Image Anal. 2020 Oct;65:101764. doi: 10.1016/j.media.2020.101764. Epub 2020 Jul 7.
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.
HEp-2 细胞模式分类在人体自身免疫性疾病的间接免疫荧光检测中起着重要作用。近年来已经提出了许多基于自动 HEp-2 细胞分类的方法，其中基于深度学习的方法表现出了令人印象深刻的性能。本文对现有的基于深度学习的 HEp-2 细胞图像分类方法进行了全面的回顾。这些方法在细胞水平和标本水平上进行 HEp-2 图像分类。在这篇综述中涵盖了这两个水平。在每个水平上，方法都是基于深度网络使用的分类法进行组织的。对每种方法的核心思想、显著成就以及关键优缺点进行了批判性分析。此外，还简要回顾了文献中常用的现有的 HEp-2 数据集。本文最后讨论了该领域的新机遇和未来研究方向。希望本文能为读者提供对这一新颖、具有挑战性和蓬勃发展的领域的全面参考。
