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Title:Breast cancer diagnosis using Fourier transform infrared imaging and statistical learning
Author(s):Mittal, Shachi
Director of Research:Bhargava, Rohit
Doctoral Committee Chair(s):Bhargava, Rohit
Doctoral Committee Member(s):Pan, Dipanjan; Balla, Andre Kajdacsy; Smith, Andrew
Department / Program:Bioengineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Breast Pathology
Machine Learning, Infrared Spectroscopic Imaging
Abstract:Cancer alters both the morphological and the biochemical properties of multiple cell types in a tissue. Generally, the morphology of epithelial cells is practically used for routine disease diagnoses. Current histopathological diagnosis involves manual interpretation of stained images for patient diagnosis. This is prone to inter- observer variability leading to low concordance rates amongst pathologists. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use of associated stromal changes is limited. To overcome these challenges, digital analysis of these images is suggested that can result in the determination of precise and quantitative metrics both for epithelial and stromal disease signatures. In my dissertation work, I focused on building combinatorial approaches using chemical imaging, histopathology images, machine learning and deep learning. An emerging area of investigation is using spectrometry to perform tissue analysis that utilizes chemical imaging coupled to machine learning to identify spectral signatures indicative of disease state and its progression. Infrared spectroscopic imaging biochemically characterizes breast cancer, both for the epithelial cells and the tumor-associated microenvironment. I utilized multiple breast tissue assignments and a supervised learning approach to create different histologic and pathologic models using both high definition (HD) and standard definition (SD) data. The comparison of HD and SD modalities shows that new information richness associated with better spatial resolution facilitates the creation of complex, multiclass models of breast tissue without compromising on the sensitivity and the specificity of tissue segmentation. These models were then extended to discrete frequency measurements for rapid analysis cutting down tissue analysis time from days to minutes, making the technology feasible for research optimizations and clinical translation. Additionally, I optimized and tuned existing convolutional neural networks to identify different disease states in breast cancer and the corresponding microenvironment. Finally, I developed analytical tools for early detection and standardized analysis of stained image data. This can offer new opportunities for objective, accurate and comprehensive patient diagnosis and prognostics.
Issue Date:2019-07-11
Rights Information:Copyright 2019 Shachi Mittal
Date Available in IDEALS:2019-11-26
Date Deposited:2019-08

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