{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108587"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108587","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Synthesizing a complete tomographic study with nodules from multiple radiograph views via deep generative models","abstract":"Chest radiography encodes a 3D anatomy into a complex 2D representation. This projection creates distinctive challenges even for the most experienced radiologists as many critical findings are superimposed, often resulting in error or further imaging. In particular, it is difficult to visualize the volume and density of lung nodules in chest radiographs. A deep generative model, commonly used to synthesize realistic images, can be used to perform 2D to 3D translations. In this thesis, we propose a generative model, optimized using pixel-wise error, that can synthesize a complete tomographic study containing nodules from frontal and lateral chest x-ray radiographs. Additionally, the generated studies maintain the proper chest cavity structure.","abstract_html":"Chest radiography encodes a 3D anatomy into a complex 2D representation. This projection creates distinctive challenges even for the most experienced radiologists as many critical findings are superimposed, often resulting in error or further imaging. In particular, it is difficult to visualize the volume and density of lung nodules in chest radiographs. A deep generative model, commonly used to synthesize realistic images, can be used to perform 2D to 3D translations. In this thesis, we propose a generative model, optimized using pixel-wise error, that can synthesize a complete tomographic study containing nodules from frontal and lateral chest x-ray radiographs. Additionally, the generated studies maintain the proper chest cavity structure.","abstract_has_math":false,"creators":["Chen, Andrew"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Koyejo, Oluwasanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T22:44:27Z","date_published":"2020-10-07T22:44:27Z","updated_at":"2026-07-22T22:24:48Z","subjects":["radiography","machine learning","generative modeling","synthesis"],"languages":["en"],"rights":["Copyright 2020 Andrew Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108587","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Oluwasanmi"]},{"key":"dc:creator","label":"Author","values":["Chen, Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-10-07T22:44:27Z","2022-10-07T22:44:53Z","2020-07-09","2020-08"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["radiography","machine learning","generative modeling","synthesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Andrew Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108587"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Chest radiography encodes a 3D anatomy into a complex 2D representation. This projection creates distinctive challenges even for the most experienced radiologists as many critical findings are superimposed, often resulting in error or further imaging. In particular, it is difficult to visualize the volume and density of lung nodules in chest radiographs. A deep generative model, commonly used to synthesize realistic images, can be used to perform 2D to 3D translations. In this thesis, we propose a generative model, optimized using pixel-wise error, that can synthesize a complete tomographic study containing nodules from frontal and lateral chest x-ray radiographs. 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This projection creates distinctive challenges even for the most experienced radiologists as many critical findings are superimposed, often resulting in error or further imaging. In particular, it is difficult to visualize the volume and density of lung nodules in chest radiographs. A deep generative model, commonly used to synthesize realistic images, can be used to perform 2D to 3D translations. In this thesis, we propose a generative model, optimized using pixel-wise error, that can synthesize a complete tomographic study containing nodules from frontal and lateral chest x-ray radiographs. 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