Deep learning based frameworks for patient selection

Abstract

Recently, deep learning (DL) has become a spearhead for solving many problems in the computer vision domain, with computational pathology (CP) has no exception. In the CP domain, it is widely used for histological assessment of tissue for diagnosis and prognosis of cancer patients. The research community has developed an abundance of DL based CP tools, reporting state-of-the-art results, for many diverse applications. In near future, we can envisage better tools on the way forward to the clinical workflow to assist pathologists in making diagnostic and anti-cancer therapeutic decisions. In this thesis, we develop DL based frameworks defining eligibility criteria for selecting patients of two different types of cancers: bladder and colorectal cancers. We develop our first framework with the main goal to investigate an automated alternative to risk stratification of urine cytology slides. We utilised digital cell profiles for the identification of patients with low-risk and high-risk of developing bladder cancer. Our experiments demonstrate that the digital risk could be a better predictor of the final histopathology based diagnosis. We then develop our second framework for the assessment of mismatch repair (MMR) status to identify patients with microsatellite instability (MSI), known to respond well to immunotherapy. We perform multi-stain tissue analysis using slides stained for MMR protein, in addition to H&E and cytokeratin stained slides. To the best of our knowledge, it is the first time that MMR status is utilised for MSI prediction. Registration is an important pre-requisite task before this multi-stain slide analysis. To this end, we present two approaches utilising two different features, hand-crafted and data-driven features. We adopted a multi-scale and multi-stage strategy, important for improving the quality of registration. These methods are able to align the images with low registration error as compared to other hand-crafted based approaches