{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120292"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120292","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Digital chemical pathology","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Falahkheirkhah, Kianoush"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Chemical Engineering","degree_department":null,"school":null,"contributors":["Bhargava, Rohit","Zhao, Huimin","Rao, Christopher V","Harley, Brendan A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Chemical Imaging","Digital Pathology","Machine Learning","Computer Vision"],"languages":["en","eng"],"rights":["Copyright 2023 Kianoush Falahkheirkhah"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120292","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bhargava, Rohit","Zhao, Huimin","Rao, Christopher V","Harley, Brendan A"]},{"key":"dc:creator","label":"Author","values":["Falahkheirkhah, Kianoush"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-24"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemical Engineering"]},{"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":["Chemical Imaging","Digital Pathology","Machine Learning","Computer Vision"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Kianoush Falahkheirkhah"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120292"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Kianoush Falahkheirkhah, accepted the attached license on 2023-04-19 at 18:10.","The student, Kianoush Falahkheirkhah, submitted this Dissertation for approval on 2023-04-19 at 18:15.","This Dissertation was approved for publication on 2023-04-24 at 15:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19051 on 2023-09-01 at 17:09:03","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Digital chemical pathology"]}]}],"canonical_facts":{"dc:contributor":["Bhargava, Rohit","Zhao, Huimin","Rao, Christopher V","Harley, Brendan A"],"dc:creator":["Falahkheirkhah, Kianoush"],"dc:date":["2023-05","2023-04-24"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Kianoush Falahkheirkhah, accepted the attached license on 2023-04-19 at 18:10.","The student, Kianoush Falahkheirkhah, submitted this Dissertation for approval on 2023-04-19 at 18:15.","This Dissertation was approved for publication on 2023-04-24 at 15:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19051 on 2023-09-01 at 17:09:03","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120292"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Kianoush Falahkheirkhah"],"dc:subject":["Chemical Imaging","Digital Pathology","Machine Learning","Computer Vision"],"dc:title":["Digital chemical pathology"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Chemical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}