{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/112323"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/112323","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Eclipse: a feedback-driven framework and automated quality assessment system for reliable, interpretable, and multi-modal biomedical AI","abstract":"This dissertation presents ECLIPSE (Evaluation and Clinical Pipeline for Intelligent Systems in Cancer Engineering), an overarching framework for reliable, interpretable, and resource-aware biomedical AI. ECLIPSE comprises two main components: (i) the CancerAI Workflow Assistant, a stage-gated web application that structures the AI development lifecycle into four coupled stages—Planning, Data, Modeling, and Inference—with measurable acceptance bands and closed-loop feedback; and (ii) MERIT-C (Medical Evidence and Reporting Index for Technical Cancer AI), the quality-assessment subsystem embedded within ECLIPSE that operationalizes the same four stages as a standardized scoring rubric for evaluating published cancer AI research. MERIT-C combines a Reporting Quality Score (RQS, 59 points across the four ECLIPSE stages) with a Technical AI Merit Score (TAMS, seven dimensions: Novelty, Architecture Complexity, Parameter Efficiency, Hardware Resource Characterization, Scalability, Generalizability, and Adaptability) under the composite MERIT-C = α · RQSₙₒᵣₘ + β · TAMSₙₒᵣₘ. Validated against 89 peer-review scores from OpenReview (Spearman ρ = 0.346, p = 0.015; AUC-ROC = 0.852, p = 0.001), MERIT-C achieves Good-class discrimination between accepted and rejected cancer AI papers. Applied to the dissertation’s own published papers, MERIT-C yields composite scores of 62.4–64.2 for the oral cancer WSI papers (OCU-Net, AUSAM, OCANet/LGA), with strongest performance on Architecture Complexity (75.0%) and weakest on Hardware Reporting (33.3%); 49.7–58.8 for the EEG papers (DL-IoT, ETSNet), with notable strength in Adaptability (87.5%) and a gap in Inference-stage reporting (27.8–44.4%); and 44.1–57.7 for the MRI/CT papers (Auto Claustrum, CU-Net, AM-UNet, TLU-Net), lagging the field on Novelty (−24.6 pp) and Hardware Reporting (−24.4 pp). The CancerAI Workflow Assistant is instantiated across three modality-specific case studies. Histopathology (oral cancer): OCANet achieves Dice 86.14 on ORCA and 94.09 on OCDC; AUSAM raises case-level Dice from 23.72 to 87.59 (ORCA) and 91.84 (OCDC). EEG (psychiatric/BCI): task-matched 1D CNNs reach 91.25% accuracy; fusion of activity-specific experts achieves 99.57% multi-class accuracy; subject-specific transfer improves from 72.82% to 93.33%. MRI/CT: CU-Net achieves liver Dice 0.894; the 0.34M-parameter AM-UNet attains Dice 0.82 and ICC = 0.90; TLU-Net demonstrates consistent cross-dataset gains via transfer learning. Together, the two components of ECLIPSE close complementary gaps: the Workflow Assistant provides the principled development pipeline, while MERIT-C provides the externally validated quality instrument, collectively narrowing the distance between algorithmic performance and clinically deployable, auditable AI.","abstract_html":"This dissertation presents ECLIPSE (Evaluation and Clinical Pipeline for Intelligent Systems in Cancer Engineering), an overarching framework for reliable, interpretable, and resource-aware biomedical AI. ECLIPSE comprises two main components: (i) the CancerAI Workflow Assistant, a stage-gated web application that structures the AI development lifecycle into four coupled stages—Planning, Data, Modeling, and Inference—with measurable acceptance bands and closed-loop feedback; and (ii) MERIT-C (Medical Evidence and Reporting Index for Technical Cancer AI), the quality-assessment subsystem embedded within ECLIPSE that operationalizes the same four stages as a standardized scoring rubric for evaluating published cancer AI research. MERIT-C combines a Reporting Quality Score (RQS, 59 points across the four ECLIPSE stages) with a Technical AI Merit Score (TAMS, seven dimensions: Novelty, Architecture Complexity, Parameter Efficiency, Hardware Resource Characterization, Scalability, Generalizability, and Adaptability) under the composite MERIT-C = α · RQSₙₒᵣₘ + β · TAMSₙₒᵣₘ. Validated against 89 peer-review scores from OpenReview (Spearman ρ = 0.346, p = 0.015; AUC-ROC = 0.852, p = 0.001), MERIT-C achieves Good-class discrimination between accepted and rejected cancer AI papers. Applied to the dissertation’s own published papers, MERIT-C yields composite scores of 62.4–64.2 for the oral cancer WSI papers (OCU-Net, AUSAM, OCANet/LGA), with strongest performance on Architecture Complexity (75.0%) and weakest on Hardware Reporting (33.3%); 49.7–58.8 for the EEG papers (DL-IoT, ETSNet), with notable strength in Adaptability (87.5%) and a gap in Inference-stage reporting (27.8–44.4%); and 44.1–57.7 for the MRI/CT papers (Auto Claustrum, CU-Net, AM-UNet, TLU-Net), lagging the field on Novelty (−24.6 pp) and Hardware Reporting (−24.4 pp). The CancerAI Workflow Assistant is instantiated across three modality-specific case studies. Histopathology (oral cancer): OCANet achieves Dice 86.14 on ORCA and 94.09 on OCDC; AUSAM raises case-level Dice from 23.72 to 87.59 (ORCA) and 91.84 (OCDC). EEG (psychiatric/BCI): task-matched 1D CNNs reach 91.25% accuracy; fusion of activity-specific experts achieves 99.57% multi-class accuracy; subject-specific transfer improves from 72.82% to 93.33%. MRI/CT: CU-Net achieves liver Dice 0.894; the 0.34M-parameter AM-UNet attains Dice 0.82 and ICC = 0.90; TLU-Net demonstrates consistent cross-dataset gains via transfer learning. Together, the two components of ECLIPSE close complementary gaps: the Workflow Assistant provides the principled development pipeline, while MERIT-C provides the externally validated quality instrument, collectively narrowing the distance between algorithmic performance and clinically deployable, auditable AI.","abstract_has_math":false,"creators":["Shah, Syed Jawad Hussain"],"institution":"University of Missouri--Kansas City","degree_name":"Ph.D. (Doctor of Philosophy)","degree_level":"Doctoral","degree_discipline":"Computer Science (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Lee, Yugyung, 1960-"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T05:16:44Z","subjects":[],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/112323","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lee, Yugyung, 1960-"]},{"key":"dc:creator","label":"Author","values":["Shah, Syed Jawad Hussain"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-22T19:12:05Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-06-22T19:12:05Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D. (Doctor of Philosophy)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/112323"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page, viewed June 29, 2026","Dissertation advisor: Yugyung Lee","Vita","Includes bibliographical references (pages 158-172)","Dissertation (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2026"]},{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation presents ECLIPSE (Evaluation and Clinical Pipeline for Intelligent Systems in Cancer Engineering), an overarching framework for reliable, interpretable, and resource-aware biomedical AI. ECLIPSE comprises two main components: (i) the CancerAI Workflow Assistant, a stage-gated web application that structures the AI development lifecycle into four coupled stages—Planning, Data, Modeling, and Inference—with measurable acceptance bands and closed-loop feedback; and (ii) MERIT-C (Medical Evidence and Reporting Index for Technical Cancer AI), the quality-assessment subsystem embedded within ECLIPSE that operationalizes the same four stages as a standardized scoring rubric for evaluating published cancer AI research. MERIT-C combines a Reporting Quality Score (RQS, 59 points across the four ECLIPSE stages) with a Technical AI Merit Score (TAMS, seven dimensions: Novelty, Architecture Complexity, Parameter Efficiency, Hardware Resource Characterization, Scalability, Generalizability, and Adaptability) under the composite MERIT-C = α · RQSₙₒᵣₘ + β · TAMSₙₒᵣₘ. Validated against 89 peer-review scores from OpenReview (Spearman ρ = 0.346, p = 0.015; AUC-ROC = 0.852, p = 0.001), MERIT-C achieves Good-class discrimination between accepted and rejected cancer AI papers. Applied to the dissertation’s own published papers, MERIT-C yields composite scores of 62.4–64.2 for the oral cancer WSI papers (OCU-Net, AUSAM, OCANet/LGA), with strongest performance on Architecture Complexity (75.0%) and weakest on Hardware Reporting (33.3%); 49.7–58.8 for the EEG papers (DL-IoT, ETSNet), with notable strength in Adaptability (87.5%) and a gap in Inference-stage reporting (27.8–44.4%); and 44.1–57.7 for the MRI/CT papers (Auto Claustrum, CU-Net, AM-UNet, TLU-Net), lagging the field on Novelty (−24.6 pp) and Hardware Reporting (−24.4 pp). The CancerAI Workflow Assistant is instantiated across three modality-specific case studies. Histopathology (oral cancer): OCANet achieves Dice 86.14 on ORCA and 94.09 on OCDC; AUSAM raises case-level Dice from 23.72 to 87.59 (ORCA) and 91.84 (OCDC). EEG (psychiatric/BCI): task-matched 1D CNNs reach 91.25% accuracy; fusion of activity-specific experts achieves 99.57% multi-class accuracy; subject-specific transfer improves from 72.82% to 93.33%. MRI/CT: CU-Net achieves liver Dice 0.894; the 0.34M-parameter AM-UNet attains Dice 0.82 and ICC = 0.90; TLU-Net demonstrates consistent cross-dataset gains via transfer learning. Together, the two components of ECLIPSE close complementary gaps: the Workflow Assistant provides the principled development pipeline, while MERIT-C provides the externally validated quality instrument, collectively narrowing the distance between algorithmic performance and clinically deployable, auditable AI."]},{"key":"dc:title","label":"Title","values":["Eclipse: a feedback-driven framework and automated quality assessment system for reliable, interpretable, and multi-modal biomedical AI"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lee, Yugyung, 1960-"],"dc:creator":["Shah, Syed Jawad Hussain"],"dc:date.accessioned":["2026-06-22T19:12:05Z"],"dc:date.available":["2026-06-22T19:12:05Z"],"dc:date.issued":["2026"],"dc:description":["Title from PDF of title page, viewed June 29, 2026","Dissertation advisor: Yugyung Lee","Vita","Includes bibliographical references (pages 158-172)","Dissertation (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2026"],"dc:description.abstract":["This dissertation presents ECLIPSE (Evaluation and Clinical Pipeline for Intelligent Systems in Cancer Engineering), an overarching framework for reliable, interpretable, and resource-aware biomedical AI. ECLIPSE comprises two main components: (i) the CancerAI Workflow Assistant, a stage-gated web application that structures the AI development lifecycle into four coupled stages—Planning, Data, Modeling, and Inference—with measurable acceptance bands and closed-loop feedback; and (ii) MERIT-C (Medical Evidence and Reporting Index for Technical Cancer AI), the quality-assessment subsystem embedded within ECLIPSE that operationalizes the same four stages as a standardized scoring rubric for evaluating published cancer AI research. MERIT-C combines a Reporting Quality Score (RQS, 59 points across the four ECLIPSE stages) with a Technical AI Merit Score (TAMS, seven dimensions: Novelty, Architecture Complexity, Parameter Efficiency, Hardware Resource Characterization, Scalability, Generalizability, and Adaptability) under the composite MERIT-C = α · RQSₙₒᵣₘ + β · TAMSₙₒᵣₘ. Validated against 89 peer-review scores from OpenReview (Spearman ρ = 0.346, p = 0.015; AUC-ROC = 0.852, p = 0.001), MERIT-C achieves Good-class discrimination between accepted and rejected cancer AI papers. Applied to the dissertation’s own published papers, MERIT-C yields composite scores of 62.4–64.2 for the oral cancer WSI papers (OCU-Net, AUSAM, OCANet/LGA), with strongest performance on Architecture Complexity (75.0%) and weakest on Hardware Reporting (33.3%); 49.7–58.8 for the EEG papers (DL-IoT, ETSNet), with notable strength in Adaptability (87.5%) and a gap in Inference-stage reporting (27.8–44.4%); and 44.1–57.7 for the MRI/CT papers (Auto Claustrum, CU-Net, AM-UNet, TLU-Net), lagging the field on Novelty (−24.6 pp) and Hardware Reporting (−24.4 pp). The CancerAI Workflow Assistant is instantiated across three modality-specific case studies. Histopathology (oral cancer): OCANet achieves Dice 86.14 on ORCA and 94.09 on OCDC; AUSAM raises case-level Dice from 23.72 to 87.59 (ORCA) and 91.84 (OCDC). EEG (psychiatric/BCI): task-matched 1D CNNs reach 91.25% accuracy; fusion of activity-specific experts achieves 99.57% multi-class accuracy; subject-specific transfer improves from 72.82% to 93.33%. MRI/CT: CU-Net achieves liver Dice 0.894; the 0.34M-parameter AM-UNet attains Dice 0.82 and ICC = 0.90; TLU-Net demonstrates consistent cross-dataset gains via transfer learning. Together, the two components of ECLIPSE close complementary gaps: the Workflow Assistant provides the principled development pipeline, while MERIT-C provides the externally validated quality instrument, collectively narrowing the distance between algorithmic performance and clinically deployable, auditable AI."],"dc:identifier.uri":["https://hdl.handle.net/10355/112323"],"dc:language.iso":["en_US"],"dc:title":["Eclipse: a feedback-driven framework and automated quality assessment system for reliable, interpretable, and multi-modal biomedical AI"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science (UMKC)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D. (Doctor of Philosophy)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:16:44Z"}