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Channel Boosted Convolutional Neural Network for Classification of Mitotic Nuclei using Histopathological Images

Development of a diagnostic model using deep learning techniques is one of the eminent areas of research in computer aided diagnostic (CAD) systems. CAD systems based on deep learning have been developed to automate the process of mitosis classification. However, mitosis classification usually suffers from class imbalance problem, moreover, high similarity between mitotic and non-mitotic nuclei as well as discrepancy in appearance and shape make classification of mitotic nuclei a challenging area of research. Quality of CAD systems that use medical images for diagnosis, highly depends on how image features are manipulated in order to get better results. This work suggests a new technique of Channel Boosted Convolutional Neural Network (CB-CNN) to classify breast cancer mitotic nuclei. In this technique, feature representation of a CNN is boosted by adding auxiliary feature channels along with original feature space (Red, Green and Blue channels) to increase the generalization of model for heterogeneous and sparse data. In the proposed method, initially (80x80) patches of mitotic and non-mitotic nuclei were extracted from histopathological images by performing histogram based binary thresholding. For the development of a CB-CNN, the potential of auxiliary feature learners was exploited to learn high-level feature representation. In this regard, an additional set of texture and gradient based feature channels were concatenated with original RGB features space of data. This boosted representation was assigned to a custom made deep CNN model. Learning capacity of the proposed CB-CNN is evaluated on TUPAC'16 challenge dataset. Channel Boosting based CNNs (0.53 for CB-CNN, 0.71 for CB-VGG and 0.54 for CB-ResNet) show improved performance in terms of F-score as compared to SVM (F-score: 0.42), and baseline CNN (0.47 for CNN, 0.55 for VGG and 0.44 for ResNet) classifier with and without transfer learning. The improved performance of CB-CNN suggests that the boosting of channel representation improves the generalization of a CNN by making feature space more versatile and flexible, thus making it robust towards heterogeneous data.