Detection of mitotic HEp-2 cell images: role of feature representation and classification framework under class skew.

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.