Director of Research (if dissertation) or Advisor (if thesis)
Bhargava, Rohit
Doctoral Committee Chair(s)
Zhao, Huimin
Committee Member(s)
Rao, Christopher V
Harley, Brendan A
Department of Study
Chemical & Biomolecular Engr
Discipline
Chemical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Chemical Imaging
Digital Pathology
Machine Learning
Computer Vision
Language
eng
Abstract
The field of pathology has relied on traditional morphological examination for decades, which can be time-consuming and costly for low-resource institutions. However, recent advancements in digital pathology and machine learning have shown promise in streamlining the process and improving healthcare outcomes for patients. In this thesis, we introduce a new technique called "Digital Chemical Pathology" (DCP), which integrates label-free imaging methods like chemical imaging with innovative machine learning techniques to measure and analyze both the morphology and chemistry of pathology samples. By being sensitive to chemical properties and considering morphology, DCP aims to provide a more comprehensive molecular analysis of tissue and aid in diagnosis and prognosis. As a result of DCP, we expect to alter the current workflow of pathology, making it faster, more accurate, and more accessible to a wider range of patients, ultimately enhancing healthcare for everyone.
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