{"id":"W2069031798","doi":"10.1109/isspa.2012.6310542","title":"Multi scale classification approach for coronary artery detection from X-ray angiography","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Hessian matrix; Robustness (evolution); False positive paradox; Angiography; Artificial intelligence; Computer science; Feature extraction; Coronary arteries; Radiology; Support vector machine; Pattern recognition (psychology); Computer vision; Artery; Medicine; Mathematics; Internal 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":[0.0008148627,0.0004991155,0.0008027725,0.001856027,0.0004900238,0.000720424,0.0010902,0.001123251,0.001740293],"category_scores_gemma":[0.001765922,0.0002946356,0.0008477714,0.001298669,0.0003591517,0.0007416388,0.0006267949,0.0008112752,0.001084415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004644926,"about_ca_system_score_gemma":0.0005309385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002130645,"about_ca_topic_score_gemma":0.002795218,"domain_scores_codex":[0.999155,0.0001532798,0.00007333162,0.0001912013,0.0003493726,0.00007780795],"domain_scores_gemma":[0.9992595,0.0002367635,0.00008265038,0.0001125902,0.0002718471,0.00003656964],"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":[0.0001635433,0.0001182252,0.001467219,0.0001236498,0.00007255932,0.0001195638,0.0000798046,0.03407003,0.05056391,0.005770778,0.002158747,0.9052919],"study_design_scores_gemma":[0.000009782574,0.00007187964,0.00251982,0.00001424108,0.000032542,0.0002252291,0.00002508639,0.9753963,0.0138174,0.004261194,0.003594947,0.00003163623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008784322,0.0003123173,0.9895809,0.00007490727,0.00004292699,0.00003270715,0.00002863964,0.0006555195,0.0004876411],"genre_scores_gemma":[0.1590705,0.0004029576,0.8380449,0.00009599895,0.00009051923,0.0001182349,0.0001881281,0.00008530591,0.00190346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002130645,"threshold_uncertainty_score":0.005821884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04363235645961141,"score_gpt":0.2826700944231824,"score_spread":0.2390377379635709,"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."}}