{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007869074,0.001147896,0.000646093,0.00106419,0.000685362,0.001087524,0.001599219,0.0009283366,0.005385995],"category_scores_gemma":[0.00153828,0.0006313701,0.0008860171,0.0005328016,0.0004468645,0.0005261836,0.001146532,0.001167211,0.00324358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009967178,"about_ca_system_score_gemma":0.001224671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005595542,"about_ca_topic_score_gemma":0.01007083,"domain_scores_codex":[0.9996502,0.00004485214,0.00002153183,0.0001152741,0.0001387873,0.0000291758],"domain_scores_gemma":[0.9994488,0.0001786995,0.00006357131,0.0001020499,0.0001648101,0.00004196104],"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":[0.000237046,0.000255096,0.003227653,0.00027573,0.0001245151,0.0004176624,0.0002347221,0.1770171,0.1508483,0.005768999,0.00918963,0.6524035],"study_design_scores_gemma":[0.00001864137,0.00007543163,0.001851834,0.0000214707,0.00001328294,0.000226822,0.00004525528,0.9457567,0.03654468,0.007665916,0.007742547,0.00003732555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00331868,0.00004771751,0.9897884,0.00009237338,0.00001096477,0.0001033979,0.0001876279,0.00581545,0.0006355093],"genre_scores_gemma":[0.05431014,0.0001190777,0.9419121,0.00009656032,0.00001597129,0.0003009215,0.0007388133,0.0003693586,0.002136999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005595542,"threshold_uncertainty_score":0.01801795,"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."}}