{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117805"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117805","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Quantitative pathology using deep learning","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Fanous, Michael John"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":["Anastasio, Mark A","Insana, Michael","Sutton, Brad","Dobrucki, Wawrzyniec"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Computational Imaging","Microscopy","Ai"],"languages":["en","eng"],"rights":["Copyright 2022 Michael Fanous"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117805","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anastasio, Mark A","Insana, Michael","Sutton, Brad","Dobrucki, Wawrzyniec"]},{"key":"dc:creator","label":"Author","values":["Fanous, Michael John"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-01"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Computational Imaging","Microscopy","Ai"]}]},{"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 2022 Michael Fanous"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117805"]}]},{"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 2023-04-12 without embargo terms","The student, Michael Fanous, accepted the attached license on 2022-11-29 at 16:55.","The student, Michael Fanous, submitted this Dissertation for approval on 2022-11-29 at 17:02.","This Dissertation was approved for publication on 2022-12-01 at 13:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18684 on 2023-04-12 at 07:36:06","The subfield of pathology known as ‘digital pathology’ encompasses the procedures for image acquisition, organization, distribution, labeling, and computational analysis. Digital pathology is expanding quickly and has already produced a remarkable impact in academia and business1. For research, clinical trials, telehealth, remote image processing, and overall patient treatment, the capacity to quickly transmit and evaluate a considerable volume of pathology data is revolutionary2. The use of artificial intelligence is at the center of this new wave of innovation, dieselizing the astonishing advancements in this increasingly digital climate. This Ph.D. thesis is centered around developing advanced pathology machine learning tools to enhance both the analysis and measurement of pathology samples. This dissertation provides a summary of my main pathology related research results, in chronological order, comprising an evolution in machine learning complexity applied to the quantitative assessment of pathology samples. First, I studied pancreatic ductal adenocarcinoma (PDAC) tissue fiber properties using a segmentation algorithm, which constitutes a good reference point for automating the dissection of specific features in tissue biopsies to derive clinically relevant statistics. After being approached by Abbott Laboratories to help examine the myelin content in certain areas of the brain, we used our research group’s quantitative phase imaging techniques to image 19 piglet brain tissue slides. I applied both manual segmentation schemes and deep learning methods to correlate the tissue properties with related size and diet statistics of the tissue subjects. The results exceeded our expectations in terms of discernment capabilities at the single frame level, a task altogether impossible for an expert histopathologist. Additionally, a series of blood smears slides were imaged and subjected to various deep learning devices to not only bypass the need for the standard Wright’s stain, but automatically delineate and label four major white blood cell groups. Finally, in an effort to simplify the pathology slide scanning procedure altogether, and after various attempts and approaches, we arrived at what is now termed ‘GANscan.’ This is a deep learning microscopy method that enables a thirtyfold increase in whole-slide scanning durations using standard equipment and software. This technique has recently been reviewed and declared a “transformative solution [that] can be used to further accelerate the adoption of digital pathology”3 by the eminent scientist Professor Ozcan in a review article on our technique."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Quantitative pathology using deep learning"]}]}],"canonical_facts":{"dc:contributor":["Anastasio, Mark A","Insana, Michael","Sutton, Brad","Dobrucki, Wawrzyniec"],"dc:creator":["Fanous, Michael John"],"dc:date":["2022-12","2022-12-01"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Michael Fanous, accepted the attached license on 2022-11-29 at 16:55.","The student, Michael Fanous, submitted this Dissertation for approval on 2022-11-29 at 17:02.","This Dissertation was approved for publication on 2022-12-01 at 13:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18684 on 2023-04-12 at 07:36:06","The subfield of pathology known as ‘digital pathology’ encompasses the procedures for image acquisition, organization, distribution, labeling, and computational analysis. Digital pathology is expanding quickly and has already produced a remarkable impact in academia and business1. For research, clinical trials, telehealth, remote image processing, and overall patient treatment, the capacity to quickly transmit and evaluate a considerable volume of pathology data is revolutionary2. The use of artificial intelligence is at the center of this new wave of innovation, dieselizing the astonishing advancements in this increasingly digital climate. This Ph.D. thesis is centered around developing advanced pathology machine learning tools to enhance both the analysis and measurement of pathology samples. This dissertation provides a summary of my main pathology related research results, in chronological order, comprising an evolution in machine learning complexity applied to the quantitative assessment of pathology samples. First, I studied pancreatic ductal adenocarcinoma (PDAC) tissue fiber properties using a segmentation algorithm, which constitutes a good reference point for automating the dissection of specific features in tissue biopsies to derive clinically relevant statistics. After being approached by Abbott Laboratories to help examine the myelin content in certain areas of the brain, we used our research group’s quantitative phase imaging techniques to image 19 piglet brain tissue slides. I applied both manual segmentation schemes and deep learning methods to correlate the tissue properties with related size and diet statistics of the tissue subjects. The results exceeded our expectations in terms of discernment capabilities at the single frame level, a task altogether impossible for an expert histopathologist. Additionally, a series of blood smears slides were imaged and subjected to various deep learning devices to not only bypass the need for the standard Wright’s stain, but automatically delineate and label four major white blood cell groups. Finally, in an effort to simplify the pathology slide scanning procedure altogether, and after various attempts and approaches, we arrived at what is now termed ‘GANscan.’ This is a deep learning microscopy method that enables a thirtyfold increase in whole-slide scanning durations using standard equipment and software. This technique has recently been reviewed and declared a “transformative solution [that] can be used to further accelerate the adoption of digital pathology”3 by the eminent scientist Professor Ozcan in a review article on our technique."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117805"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Michael Fanous"],"dc:subject":["Computational Imaging","Microscopy","Ai"],"dc:title":["Quantitative pathology using deep learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Bioengineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}