{"id":"W3041607742","doi":"10.23919/eucap48036.2020.9135659","title":"An Open-Access Experimental Dataset for Breast Microwave Imaging","year":2020,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Microwave imaging; Logistic regression; Computer science; Breast cancer; Imaging phantom; Artificial intelligence; Machine learning; Breast imaging; Medical imaging; Classifier (UML); Mammography; Medical physics; Data mining; Microwave; Medicine; Cancer; Radiology; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001783611,0.001645456,0.001107355,0.00177596,0.0006820503,0.001054074,0.003036669,0.002728268,0.01542766],"category_scores_gemma":[0.005717096,0.0004163794,0.001307789,0.001923327,0.0005749936,0.0005900981,0.001349497,0.00147334,0.01763759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095565,"about_ca_system_score_gemma":0.001493285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017239,"about_ca_topic_score_gemma":0.0252557,"domain_scores_codex":[0.9988347,0.0002470614,0.0001143879,0.0003435926,0.0003371732,0.0001230683],"domain_scores_gemma":[0.9972233,0.0008658668,0.0002065871,0.0007456646,0.0007732905,0.0001853265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001801019,0.001424669,0.01827354,0.00260713,0.0004523024,0.0009265202,0.0001392344,0.009199028,0.01003066,0.001895733,0.8746902,0.07855997],"study_design_scores_gemma":[0.001389779,0.0007485294,0.06794884,0.0006596142,0.0003404578,0.002726217,0.0003590375,0.0350993,0.01778484,0.005827096,0.8668689,0.0002475756],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02490413,0.001435768,0.00959551,0.0008239059,0.0004011141,0.0005130747,0.9514357,0.005867573,0.005023175],"genre_scores_gemma":[0.01794724,0.0002880875,0.01091838,0.00022944,0.00005819686,0.0005897625,0.9677346,0.0002071175,0.002027167],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01542766,"threshold_uncertainty_score":0.05161065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03969816747301863,"score_gpt":0.3360571915023514,"score_spread":0.2963590240293328,"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."}}