{"id":"W4409161079","doi":"10.1016/j.media.2025.103559","title":"Improved unsupervised 3D lung lesion detection and localization by fusing global and local features: Validation in 3D low-dose computed tomography","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Samsung Medical Center, Sungkyunkwan University; Institute for Information and Communications Technology Promotion; Seoul National University; Ministry of Health and Welfare; Ministry of Trade, Industry and Energy; Ministry of Food and Drug Safety; Korea Medical Device Development Fund","keywords":"Artificial intelligence; Computed tomography; Computer science; Computer vision; Pattern recognition (psychology); Radiology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002114603,0.001319932,0.0009371401,0.00104646,0.0003487974,0.001214115,0.001563308,0.001478201,0.001038145],"category_scores_gemma":[0.003556127,0.0005710209,0.001598928,0.0006916806,0.0007618041,0.0007326503,0.001332592,0.00127507,0.0005317017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009631257,"about_ca_system_score_gemma":0.001186215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01321264,"about_ca_topic_score_gemma":0.01435746,"domain_scores_codex":[0.9993072,0.0001698331,0.00004182522,0.000226127,0.0001734857,0.00008161381],"domain_scores_gemma":[0.9989501,0.0004670859,0.00009649725,0.0001775128,0.0002137576,0.0000951508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003677102,0.000273103,0.0145725,0.0001670068,0.0003605671,0.0001924079,0.0001150692,0.8271371,0.01698701,0.000852352,0.002286484,0.1366887],"study_design_scores_gemma":[0.00001073036,0.0000462937,0.00116143,0.000007913335,0.00001412181,0.00004819879,0.000009207159,0.9947509,0.003431857,0.0002818537,0.0002259546,0.00001149013],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4009388,0.001151027,0.585039,0.0006182826,0.0001588006,0.0003265695,0.001196882,0.008762442,0.001808097],"genre_scores_gemma":[0.857651,0.0002969077,0.137746,0.0002953799,0.00003916036,0.0001387296,0.001991612,0.0003272565,0.001513969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01321264,"threshold_uncertainty_score":0.02627146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004103300644462484,"score_gpt":0.2765436820288232,"score_spread":0.2724403813843607,"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."}}