{"id":"W2167988080","doi":"10.2528/pier11050408","title":"A BIMODAL RECONSTRUCTION METHOD FOR BREAST CANCER IMAGING","year":2011,"lang":"en","type":"article","venue":"Electromagnetic waves","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; CancerCare Manitoba","funders":"","keywords":"Electrical impedance tomography; Breast cancer; Microwave imaging; Breast imaging; Modality (human–computer interaction); Microwave; Iterative reconstruction; Radar; Artificial intelligence; Computer science; Computer vision; Tomography; Medical physics; Mammography; Medicine; Radiology; Cancer; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000132712,0.0001612335,0.0001805884,0.0001465529,0.00007086786,0.00003483388,0.000111701,0.00003331519,0.0004293634],"category_scores_gemma":[0.000006430241,0.0001629843,0.0001055979,0.0001700242,0.00003189996,0.00008575986,0.000009623846,0.0001057803,0.00001060423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000536395,"about_ca_system_score_gemma":0.00001705941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001954575,"about_ca_topic_score_gemma":0.00002221456,"domain_scores_codex":[0.9991449,0.0000244013,0.0001847904,0.0002175699,0.00006464026,0.0003636664],"domain_scores_gemma":[0.9996643,0.00002633757,0.00002835658,0.0001773347,0.00004028526,0.00006335428],"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":[0.00001672053,0.00001453158,0.0009189368,0.00006711167,0.0001160286,0.000002445452,0.0004548591,0.0001432352,0.5821843,0.00007280911,0.001606209,0.4144029],"study_design_scores_gemma":[0.001130921,0.0001612736,0.0355586,0.0001430999,0.0005332307,0.001442023,0.0002511983,0.6879342,0.264213,0.005811638,0.001592555,0.001228311],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6958238,0.004056464,0.2858682,0.0009538687,0.001169564,0.000457164,0.0001458282,0.001449414,0.01007577],"genre_scores_gemma":[0.885906,0.0001011324,0.1131589,0.0000658417,0.000201568,0.00007264927,0.0000048252,0.00005588839,0.0004331457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6877909,"threshold_uncertainty_score":0.6646308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008299625549835632,"score_gpt":0.223366087211985,"score_spread":0.2150664616621493,"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."}}