Detecting mitotic cells in HEp-2 images as anomalies via one class classifier.

We propose a novel framework for classification of mitotic v/s non-mitotic cells in a Computer Aided Diagnosis (CAD) system for Anti-Nuclear Antibodies (ANA) detection. In the proposed work, due to unique characteristics (the rare occurrence) of the mitotic cells, their identification is posed as an anomaly detection approach. This will resolve the issue of data imbalance, which can arise in the traditional binary classification paradigm for mitotic v/s non-mitotic cell image classification. Here, the characteristics of only non-mitotic/interphase cells are captured using a well-defined feature representation to characterize the non-mitotic class distribution well, and the mitotic class is posed as an anomalous class. This framework requires training data only for the majority (non-mitotic) class, to build the classification model. The feature representation of the non-mitotic class includes morphology, texture, and Convolutional Neural Network (CNN) based feature representations, coupled with Bag-of-Words (BoW) and Spatial Pyramid Pooling (SPP) based summarization techniques. For classification, in this work, we employ the One-Class Support Vector Machines (OC-SVM). The proposed classification framework is validated on a publicly available dataset, and across various experiments, we demonstrate comparable or better performance over binary classification, attaining 0.99 (max.) F-Score in one case. The proposed framework proves to be an effective way to solve the mentioned problem statement, where there are less number of samples in one of the classes.