{"id":"W4416624981","doi":"10.1016/j.compbiomed.2025.111332","title":"CL-GAN: A progressive curriculum learning approach for bone CT super-resolution","year":2025,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"Canadian Arthritis Network; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada Foundation for Innovation; Arthritis Society","keywords":"Context (archaeology); Image quality; Artifact (error); Metric (unit); Curriculum; Quantitative computed tomography; Modality (human–computer interaction); Domain (mathematical analysis)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009499757,0.0009217646,0.0004228552,0.00060897,0.0003115504,0.0006886005,0.002945848,0.001153232,0.01882374],"category_scores_gemma":[0.002139103,0.0004407266,0.0007311051,0.0004358952,0.0003296194,0.0008935908,0.001948435,0.002182625,0.003926155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005671462,"about_ca_system_score_gemma":0.001080096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790663,"about_ca_topic_score_gemma":0.01005021,"domain_scores_codex":[0.9996938,0.00007382372,0.00001397723,0.00008716688,0.00009485927,0.00003651334],"domain_scores_gemma":[0.9993349,0.0002980203,0.00002399744,0.00008958961,0.0001820666,0.0000715418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003044484,0.0005064764,0.001255863,0.0003543061,0.0000822772,0.0002274745,0.0002576562,0.1382444,0.02334227,0.0217557,0.03756924,0.7760998],"study_design_scores_gemma":[0.00006169519,0.0001119538,0.0002524124,0.00002860113,0.00001730937,0.0001278787,0.00004241644,0.956629,0.01379428,0.01115908,0.01775878,0.00001653227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00427079,0.00008005966,0.9799991,0.0001009662,0.00003837635,0.0001769273,0.0002426013,0.01023008,0.004861065],"genre_scores_gemma":[0.053878,0.000131298,0.9371952,0.0002071827,0.0000220748,0.0003472283,0.0009000929,0.001124792,0.006194162],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01882374,"threshold_uncertainty_score":0.06297171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01923998755531561,"score_gpt":0.364684474936541,"score_spread":0.3454444873812255,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}