Doctor of Philosophy, Case Western Reserve University, 2024, EECS - System and Control Engineering

This dissertation investigates the application of digital pathology for developing diagnostic and prognostic tools for 2 diseases: Biliary tract adenocarcinoma and Papillary Thyroid Carcinoma (PTC). We explore the realms of cytopathology, which studies exclusively the morphologies of epithelial cells, and histopathology, which includes the entire tissue region. Bile duct brush specimens are difficult to interpret as they often present inflammatory and reactive backgrounds due to the local effects of stricture, atypical reactive changes, or previously installed stents, and often have low to intermediate cellularity. As a result, diagnosis of biliary adenocarcinomas is challenging and often results in large interobserver variability and low sensitivity. In this dissertation, we first used computational image analysis to evaluate the role of nuclear morphological and texture features of epithelial cell clusters to predict the presence of biliary tract adenocarcinoma on digitized brush cytology specimens. We improved the sensitivity of diagnosis with a machine learning approach from 46% to 68% when atypical cases were included and treated as nonmalignant false negatives. The specificity of our model was 100% within the atypical category. PTC is the most prevalent form of thyroid cancer, with the classical form and the follicular variant representing the majority of cases. Despite generally favorable prognoses, approximately 10% of patients experience recurrence post- surgery and radioactive iodine therapy. Attempts to stratify risk of recurrence have relied on gene expression-based prognostic and predictive signatures with a focus on mutations of well-known driver genes, while hallmarks of tumor morphology have been ignored. In this dissertation, we introduce a new computational pathology approach to develop prognostic gene signatures for thyroid cancer that is informed by quantitative features of tumor and immune cell morphology. We show that integrating gene express (open full item for complete abstract)

Committee: Kenneth Loparo (Committee Chair); Anant Madabhushi (Advisor); Satish Viswanath (Committee Member); Sylvia Asa (Committee Member); Aparna Harbhajanka (Committee Member) Subjects: Artificial Intelligence; Biomedical Engineering; Biomedical Research; Biostatistics; Computer Engineering; Medical Imaging; Oncology; Systems Design

MS, University of Cincinnati, 2018, Engineering and Applied Science: Computer Science

The idea of a zero-knowledge proof was first conceived in 1985 and has since gained enormous attention in the field of cryptography. A zero-knowledge proof makes sure the prover follows the protocol and no secret information of the prover is compromised in the process of proving a statement. Zero knowledge proofs can be categorized into interactive and non - interactive zero knowledge proof systems. A non-interactive zero knowledge proof has two advantages over the interactive zero knowledge proof: it saves communication overhead as it does not require any interaction and it can be used to convince any number of observers or verifiers without interacting with each verifier. In this thesis, we study the non-interactive zero knowledge proof which requires a one-way communication. We discuss how to build interactive and non-interactive proofs based on the discrete logarithm problem. We investigate the performance of the zero-knowledge proof by comparing the computational time between non-interactive and interactive zero knowledge identification protocols based on the discrete logarithm problem. We also study the performance of a non-interactive zero knowledge digital signature scheme using well-known secure cryptographic hash functions.

Committee: Boyang Wang (Committee Chair); Yizong Cheng Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science