Graduation Year
2025
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Electrical Engineering
Major Professor
Ghulam Rasool, Ph.D.
Co-Major Professor
Yasin Yilmaz, Ph.D.
Committee Member
Issam El Naqa, Ph.D.
Committee Member
Mia Naeini, Ph.D.
Committee Member
Alex Otten, Ph.D.
Keywords
Cancer, Large Language Models, Machine Learning, Robustness, Uncertainty Quantification
Abstract
This dissertation presents a cohesive set of novel frameworks developed to address critical challenges in oncology data integration, representation learning, and clinical information extraction. The work encompasses four interconnected projects: MINDS (Multimodal Integration of Oncology Data System), HoneyBee (Harmonized ONcologY Biomedical Embedding Encoder), LLM Extraction (Large Language Model-based Extraction from Pathology Reports), and EAGLE (Embedding Analysis for Generalized Learning in Oncology). Together, these systems enable the unification of diverse cancer data modalities—from genomics and clinical records to histopathology images and radiological scans—creating a robust foundation for advanced machine learning applications in precision oncology. By addressing key barriers in data accessibility, standardization, and analysis, this work establishes a comprehensive technical infrastructure for accelerating cancer research and improving clinical decision-making through AI-augmented methodologies.
Scholar Commons Citation
Tripathi, Aakash Gireesh, "Embedding-Based Deep Learning Frameworks for Multimodal Oncology Data Integration" (2025). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10908
