{"id":"W2130880307","doi":"10.1109/42.974924","title":"Feature extraction and classification of dynamic contrast-enhanced T2*-weighted breast image data","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Region of interest; Artificial intelligence; Computer science; Pixel; Pattern recognition (psychology); Breast imaging; Noise (video); Computer vision; Feature (linguistics); Feature extraction; Data set; Contrast (vision); Noise reduction; Mammography; Breast cancer; Image (mathematics); Medicine; Cancer","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":[0.001015194,0.0005732217,0.000514189,0.001853908,0.0002099894,0.0005740101,0.0005518042,0.0004987257,0.001023586],"category_scores_gemma":[0.004869706,0.0001130544,0.0003970257,0.0008155661,0.0002890884,0.0004107542,0.0002907155,0.0003028452,0.0005437883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000257065,"about_ca_system_score_gemma":0.0003377937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001024748,"about_ca_topic_score_gemma":0.0008289599,"domain_scores_codex":[0.9995951,0.000067225,0.00006734312,0.00009995586,0.0001131172,0.00005736798],"domain_scores_gemma":[0.9983004,0.0009116812,0.0002294973,0.0001245125,0.0003771343,0.00005688142],"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":[0.001204089,0.0005341265,0.0404105,0.0005399958,0.0001300916,0.0005535161,0.0001733513,0.01028661,0.2032625,0.0003015034,0.00293749,0.7396663],"study_design_scores_gemma":[0.0001412393,0.001975556,0.3671143,0.0001428741,0.0003790903,0.003999481,0.000403523,0.3770359,0.2389518,0.001533011,0.008177039,0.0001461845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.79633,0.0009573828,0.1987689,0.0002266794,0.00009188035,0.0002288164,0.001280816,0.001167153,0.0009484447],"genre_scores_gemma":[0.8688988,0.0002964186,0.1267353,0.00005022454,0.00006120979,0.0002939631,0.002966493,0.00008132392,0.0006162839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001853908,"threshold_uncertainty_score":0.005368888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01961769740504659,"score_gpt":0.3323913412435437,"score_spread":0.3127736438384971,"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."}}