School of Computing & Electrical Engineering, Indian Institute of Technology Mandi, Mandi, India.
Med Biol Eng Comput. 2022 Aug;60(8):2405-2421. doi: 10.1007/s11517-022-02613-0. Epub 2022 Jun 30.
We propose and analyze a framework to detect and identify the mitotic type staining patterns among different non-mitotic (interphase) patterns on HEp-2 cell substrate specimen images. This is considered as a principal task in computer-aided diagnosis (CAD) of the autoimmune disorders. Due to the rare appearance of mitotic patterns in whole slide/specimen images, the sample skew between mitotic and non-mitotic patterns is an important consideration.We suggest to apply some effective samples skew balancing strategies for the task of classification between mitotic v/s interphase patterns. Another aspect of this study is to consider the morphology and texture-based differences between both the classes that can be incorporated through effective morphology and texture-based descriptors, including the Gabor and LM (Leung-Malik) filter banks and also through some contemporary filter banks derived from convolutional neural networks (CNN).The proposed framework is evaluated on a public dataset and we demonstrate good performance (0.99 or 1 Matthews correlation coefficient (MCC) in many cases), across various experiments. The study also presents a comparison between hand-engineered and CNN-based feature representation, along with the comparisons with state-of-the-art approaches. Hence, the framework proves to be a good solution for the mentioned skewed classification problem.
我们提出并分析了一个框架，以检测和识别 HEp-2 细胞基质标本图像中不同非有丝分裂（间期）模式下的有丝分裂类型染色模式。这被认为是自身免疫性疾病计算机辅助诊断（CAD）中的主要任务。由于有丝分裂模式在整个幻灯片/标本图像中很少出现，因此有丝分裂和非有丝分裂模式之间的样本倾斜是一个重要的考虑因素。我们建议针对有丝分裂与间期模式之间的分类任务应用一些有效的样本倾斜平衡策略。这项研究的另一方面是考虑两个类之间基于形态和纹理的差异，这些差异可以通过有效的形态和纹理描述符来整合，包括 Gabor 和 LM（Leung-Malik）滤波器组，以及通过源自卷积神经网络（CNN）的一些当代滤波器组来整合。该框架在一个公共数据集上进行了评估，我们在许多情况下展示了良好的性能（0.99 或 1 马修斯相关系数（MCC）），在各种实验中都表现出色。该研究还比较了基于手工和基于 CNN 的特征表示，以及与最先进方法的比较。因此，该框架被证明是解决上述倾斜分类问题的一个很好的解决方案。
