{"id":"W3138187999","doi":"10.3390/electronics10060674","title":"A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Research Manitoba","keywords":"Workflow; Permittivity; Microwave imaging; Breast imaging; Computer science; Inference; Stage (stratigraphy); Artificial neural network; Calibration; Artificial intelligence; Microwave; Physics; Dielectric; Mammography; Breast cancer; Optoelectronics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002196943,0.0001702324,0.0002104104,0.000164067,0.0000958212,0.00008170438,0.00006204951,0.00004445013,0.00001202409],"category_scores_gemma":[0.00004230145,0.0002104166,0.0001060214,0.0004156869,0.00001049696,0.00008453948,0.00002234256,0.00040045,0.00000529155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003497915,"about_ca_system_score_gemma":0.00004950719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002939304,"about_ca_topic_score_gemma":0.000475532,"domain_scores_codex":[0.9988918,0.00004323683,0.0002204009,0.0002469451,0.00008106238,0.0005165011],"domain_scores_gemma":[0.9996942,0.00004008281,0.00003249487,0.0001428431,0.00004633743,0.00004404292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007090583,0.00001204079,0.0009212137,0.00004752816,0.00004115217,0.000022527,0.0001154613,0.09978489,0.7716908,0.000004315687,0.00001654966,0.1273364],"study_design_scores_gemma":[0.0003028107,0.000007311308,0.000090187,0.00005000741,0.00003862706,0.000181714,0.00002285434,0.8051434,0.1880837,0.0001329548,0.005739338,0.0002071799],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5329601,0.02615952,0.4399538,0.000133232,0.0001845321,0.0001005781,0.000004353143,0.0002939133,0.0002099577],"genre_scores_gemma":[0.9917412,0.0003503264,0.007591367,0.00003731799,0.0000906114,0.000009887877,0.00002587444,0.0000530089,0.0001003511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7053584,"threshold_uncertainty_score":0.8580542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006068144313760296,"score_gpt":0.2118185369785766,"score_spread":0.2057503926648163,"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."}}