{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129191"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129191","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Deep learning-based M-mode OCT system and B-mode OCT system diagnosis accuracy comparison","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Tong, Linjie"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Do, Minh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-30","date_published":"2025-04-30","updated_at":"2026-07-22T22:25:04Z","subjects":["Medical Image Analysis","Deep Learning"],"languages":["en","eng"],"rights":["Copyright 2025 Linjie Tong"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129191","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh"]},{"key":"dc:creator","label":"Author","values":["Tong, Linjie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-30","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Medical Image Analysis","Deep Learning"]}]},{"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 2025 Linjie Tong"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129191"]}]},{"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 2025-10-19 without embargo terms","The student, Linjie Tong, accepted the attached license on 2025-04-28 at 22:28.","The student, Linjie Tong, submitted this Thesis for approval on 2025-04-28 at 22:34.","This Thesis was approved for publication on 2025-04-30 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21725 on 2025-10-19 at 18:09:17","M-mode Optical Coherence Tomography (OCT) imaging is a cost-effective alternative to the widely used B-mode OCT in medical imaging. However, as an emerging imaging modality, M-mode OCT has not been extensively studied for its diagnostic capabilities. In this paper, we propose a convolutional neural network (CNN)-based framework to evaluate the diagnostic performance of M-mode and B-mode OCT images. Our results demonstrate that M-mode OCT can achieve comparable diagnostic accuracy to B-mode OCT. To investigate the reason behind this comparable performance, we conduct further analysis in two parts. First, using transfer learning, we show that deep learning models extract highly similar features from both M-mode and B-mode OCT images. Second, we analyze the feature distributions and observe that both modalities yield distinguishable differences between normal and abnormal cases. These findings suggest that the critical diagnostic information in OCT images is primarily encoded in the depth profiles of individual A-scans. Motivated by this insight, we propose a weakly supervised algorithm based on Multi-Instance Learning (MIL), which extracts features from individual A-scans and integrates them to generate final diagnostic predictions. Notably, this method does not require per A-scan labels during training, yet it is capable of producing per A-scan predictions. The proposed approach achieves diagnostic performance comparable to models that utilize entire M-mode or B-mode OCT images, while offering enhanced interpretability through localized, per A-scan outputs."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Deep learning-based M-mode OCT system and B-mode OCT system diagnosis accuracy comparison"]}]}],"canonical_facts":{"dc:contributor":["Do, Minh"],"dc:creator":["Tong, Linjie"],"dc:date":["2025-04-30","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Linjie Tong, accepted the attached license on 2025-04-28 at 22:28.","The student, Linjie Tong, submitted this Thesis for approval on 2025-04-28 at 22:34.","This Thesis was approved for publication on 2025-04-30 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21725 on 2025-10-19 at 18:09:17","M-mode Optical Coherence Tomography (OCT) imaging is a cost-effective alternative to the widely used B-mode OCT in medical imaging. However, as an emerging imaging modality, M-mode OCT has not been extensively studied for its diagnostic capabilities. In this paper, we propose a convolutional neural network (CNN)-based framework to evaluate the diagnostic performance of M-mode and B-mode OCT images. Our results demonstrate that M-mode OCT can achieve comparable diagnostic accuracy to B-mode OCT. To investigate the reason behind this comparable performance, we conduct further analysis in two parts. First, using transfer learning, we show that deep learning models extract highly similar features from both M-mode and B-mode OCT images. Second, we analyze the feature distributions and observe that both modalities yield distinguishable differences between normal and abnormal cases. These findings suggest that the critical diagnostic information in OCT images is primarily encoded in the depth profiles of individual A-scans. Motivated by this insight, we propose a weakly supervised algorithm based on Multi-Instance Learning (MIL), which extracts features from individual A-scans and integrates them to generate final diagnostic predictions. Notably, this method does not require per A-scan labels during training, yet it is capable of producing per A-scan predictions. The proposed approach achieves diagnostic performance comparable to models that utilize entire M-mode or B-mode OCT images, while offering enhanced interpretability through localized, per A-scan outputs."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129191"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Linjie Tong"],"dc:subject":["Medical Image Analysis","Deep Learning"],"dc:title":["Deep learning-based M-mode OCT system and B-mode OCT system diagnosis accuracy comparison"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}