{"id":"W4319759385","doi":"10.1016/j.bspc.2023.104673","title":"An adaptively weighted ensemble of multiple CNNs for carotid ultrasound image segmentation","year":2023,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Weighting; Ultrasound; Deep learning; Ensemble learning; Medicine; Radiology","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":[0.0009212689,0.001097162,0.0012738,0.000953986,0.0004364889,0.0007510794,0.001516768,0.001458357,0.001624661],"category_scores_gemma":[0.001108554,0.0006609465,0.0009754541,0.0009155257,0.0002394046,0.0009401893,0.001193685,0.0009356166,0.0007333632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006532634,"about_ca_system_score_gemma":0.001207382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327632,"about_ca_topic_score_gemma":0.02078738,"domain_scores_codex":[0.9996177,0.00004723408,0.00001952874,0.0001304545,0.00009898748,0.00008606679],"domain_scores_gemma":[0.9996057,0.00007380617,0.00002649843,0.00005527605,0.0001975574,0.000041218],"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":[0.0003113514,0.0001864937,0.003068858,0.00008945752,0.0003981226,0.0001766571,0.00006450442,0.2336412,0.03636527,0.001673891,0.004194598,0.7198296],"study_design_scores_gemma":[0.000003520879,0.0000508605,0.0005710312,0.000006979451,0.00005980813,0.00004667035,0.00000737558,0.9942954,0.003882437,0.0004322359,0.0006370545,0.000006475993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07846861,0.002121121,0.9131141,0.0003173929,0.0003162608,0.00009012047,0.0002004668,0.00193279,0.003439063],"genre_scores_gemma":[0.6836658,0.00106984,0.3029879,0.0004234631,0.0002218903,0.0001065662,0.0007058726,0.0002073277,0.01061146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01327632,"threshold_uncertainty_score":0.02639806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231164887112998,"score_gpt":0.2687453443521357,"score_spread":0.2564336954810057,"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."}}