{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105925"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105925","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Breast cancer diagnosis using Fourier transform infrared imaging and statistical learning","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Mittal, Shachi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":["Bhargava, Rohit","Pan, Dipanjan","Balla, Andre Kajdacsy","Smith, Andrew"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:59:42Z","date_published":"2019-11-26T20:59:42Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Breast Pathology","Machine Learning, Infrared Spectroscopic Imaging"],"languages":["en"],"rights":["Copyright 2019 Shachi Mittal"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105925","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bhargava, Rohit","Pan, Dipanjan","Balla, Andre Kajdacsy","Smith, Andrew"]},{"key":"dc:creator","label":"Author","values":["Mittal, Shachi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:59:42Z","2023-11-26T06:00:00Z","2019-07-11","2019-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Bioengineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Breast Pathology","Machine Learning, Infrared Spectroscopic Imaging"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Shachi Mittal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105925"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-08-01","The student, Shachi Mittal, accepted the attached license on 2019-07-10 at 09:35.","The student, Shachi Mittal, submitted this Dissertation for approval on 2019-07-10 at 15:26.","This Dissertation was approved for publication on 2019-07-11 at 13:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14241 on 2019-11-26 at 14:03:23","Made available in DSpace on 2019-11-26T20:59:42Z (GMT). No. of bitstreams: 2 MITTAL-DISSERTATION-2019.pdf: 6909092 bytes, checksum: 289f8ec7d3af773587c848c03be374ee (MD5) LICENSE.txt: 4210 bytes, checksum: 282484ffe017e9cf19ac33938290f8bc (MD5) Previous issue date: 2019-07-11","Embargo set by: Seth Robbins for item 113072 Lift date: 2021-11-26T20:59:54Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction set for Item 113072 on 2021-07-09T15:16:01Z with date 2023-11-26 by astein@illinois.edu.","Limited Restriction set for Item 113072 on 2021-07-09T15:16:05Z with date 2023-11-26 by astein@illinois.edu.","Author submitted a closed access extension request that was approved by the Thesis Office.","Limited"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Breast cancer diagnosis using Fourier transform infrared imaging and statistical learning"]}]}],"canonical_facts":{"dc:contributor":["Bhargava, Rohit","Pan, Dipanjan","Balla, Andre Kajdacsy","Smith, Andrew"],"dc:creator":["Mittal, Shachi"],"dc:date":["2019-11-26T20:59:42Z","2023-11-26T06:00:00Z","2019-07-11","2019-08"],"dc:description":["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.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-08-01","The student, Shachi Mittal, accepted the attached license on 2019-07-10 at 09:35.","The student, Shachi Mittal, submitted this Dissertation for approval on 2019-07-10 at 15:26.","This Dissertation was approved for publication on 2019-07-11 at 13:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14241 on 2019-11-26 at 14:03:23","Made available in DSpace on 2019-11-26T20:59:42Z (GMT). No. of bitstreams: 2 MITTAL-DISSERTATION-2019.pdf: 6909092 bytes, checksum: 289f8ec7d3af773587c848c03be374ee (MD5) LICENSE.txt: 4210 bytes, checksum: 282484ffe017e9cf19ac33938290f8bc (MD5) Previous issue date: 2019-07-11","Embargo set by: Seth Robbins for item 113072 Lift date: 2021-11-26T20:59:54Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction set for Item 113072 on 2021-07-09T15:16:01Z with date 2023-11-26 by astein@illinois.edu.","Limited Restriction set for Item 113072 on 2021-07-09T15:16:05Z with date 2023-11-26 by astein@illinois.edu.","Author submitted a closed access extension request that was approved by the Thesis Office.","Limited"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105925"],"dc:language":["en"],"dc:rights":["Copyright 2019 Shachi Mittal"],"dc:subject":["Breast Pathology","Machine Learning, Infrared Spectroscopic Imaging"],"dc:title":["Breast cancer diagnosis using Fourier transform infrared imaging and statistical learning"],"dc:type":["text"],"thesis:degree_discipline":["Bioengineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:45Z"}