Deep Learning-Driven Diagnostic and Prognostic Solutions for Histopathological Images of Bladder Cancer

Abstract

This thesis presents a comprehensive investigation into the development and application of advanced computational techniques for the extraction of crucial diagnostic and prognostic information from histological images of non-muscle invasive bladder cancer (NMIBC). Computational pathology (CPATH) relies on digitized high-resolution tissue samples, referred to as whole slide images (WSIs). Histological examination of WSIs plays a pivotal role in the diagnosis and prognosis of NMIBC. The primary focus of this research is the utilization of deep learning algorithms to automatically analyze histological images and extract visual cues with diagnostic and prognostic significance. With respect to diagnostics, several convolutional neural network architectures are designed and trained on diverse datasets of NMIBC tissue specimens to identify and classify key histological features, including tumor grading and staging. Moreover, the variability of histological visual features between pathology laboratories during the training of convolutional neural network (CNN) models is questioned. Emphasis is placed on the development of label-efficient guidelines for domain-adapting deep learning models. In addition, an architecture for machine learning is introduced to stratify regions of interest (ROIs) in weakly supervised learning. This additional data stratification aids in localizing ROIs and mitigating cross-noise variability among them. Furthermore, this thesis explores the integration of deep learning techniques for prognostic assessment. Through an analysis of the relative spatial distribution among urothelium and contingent stromal immune cells, our model predicts patient treatment outcomes and the likelihood of recurrence with a high degree of precision. These prognostic models provide invaluable support to clinicians in customizing personalized treatment strategies and offering patient counseling. The work presented in this thesis represents a substantial advancement toward improving the diagnostic and prognostic capabilities in the management of NMIBC. Leveraging the potential of computational analysis, we offer pathologists state-of-the-art tools to augment diagnostic precision and optimize patient care, ultimately contributing to better outcomes and quality of life for individuals affected by this prevalent form of bladder cancer.