A Robust Ensemble Framework for Mitosis Detection in Histopathology Images

In digital pathology, computer-assisted techniques are acquired to view, manage, and analyze images taken under a microscope, commonly referred to as whole slide images. Using artificial intelligence-based algorithms for automatic diagnosis, the field is rapidly evolving. An important subfield of automatic diagnosis is the identification of cells in mitosis in whole slide images. Mitotic score determines tumour aggressiveness by grading histopathological images. Mitosis score is a critical component of treatment decisions based on histopathological images. Mitosis counting manually is extremely tedious, but automated methods can eliminate inefficiencies and subjectivity. A mitosis detection algorithm based on an ensemble of segmentation and detection methods is presented in this thesis. Using deep learning methods, we implement an ensemble algorithm while overcoming inadequate and complex training data. The proposed deep learning pipeline comprises regions of interest locator, an adversarial network for the segmentation, a regional convolutional network method for detecting cells in mitosis, and an ensemble model to incorporate segmentation and detection models. Mitosis results from segmentation and detection parts are fused by merging all the predictions from multiple models using weighted box fusion method. In order to improve the performance and capabilities of the model, techniques such as augmentation, normalization, and sampling are considered. In addition, to have a pixel-based annotation dataset, a semi-supervised pseudo labelling method is considered. We achieved promising results, demonstrating the power of the proposed machine learning and data enhancement methods.