The authors propose a novel HEp-2 cell image classifier to improve the automation process of patients' serum evaluation. The authors' solution builds on the recent progress in deep learning based image classification. They propose an ensemble approach using multiple state-of-the-art architectures. They incorporate additional texture information extracted by an improved version of local binary patterns maps, $\alpha $α LBP-maps, which enables to create a very effective cell image classifier. This innovative combination is trained on three publicly available datasets and its general applicability is demonstrated through the evaluation on three independent test sets. The presented results show that their approach leads to a general improvement of performance on average on the three public datasets.

中文翻译：

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使用局部二进制模式图的HEp-2细胞识别的通用深度学习框架

作者提出了一种新颖的HEp-2细胞图像分类器，以改善患者血清评估的自动化过程。作者的解决方案基于基于深度学习的图像分类的最新进展。他们提出了使用多种最新体系结构的集成方法。它们合并了通过改进版本的本地二进制模式图提取的其他纹理信息，$ \ alpha $α LBP贴图，可以创建非常有效的细胞图像分类器。这种创新的组合在三个可公开获得的数据集上进行了训练，并且通过对三个独立测试集的评估证明了其总体适用性。提出的结果表明，他们的方法可以使三个公共数据集的性能平均提高。