{"id":"W2803012811","doi":"10.1109/icassp.2018.8461901","title":"Coherent Time Reversal Sub-Array Processing for Microwave Breast Imaging","year":2018,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Clutter; Finite-difference time-domain method; Microwave imaging; Beamforming; Focus (optics); Computer science; Signal processing; Breast cancer; Time–frequency analysis; Microwave; Aperture (computer memory); Synthetic aperture radar; Acoustics; Artificial intelligence; Radar; Telecommunications; Optics; Physics; Cancer; Medicine","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":[0.000171596,0.0001932205,0.000202374,0.0001117041,0.0001303231,0.0001332294,0.0001512378,0.00003600167,0.0001840974],"category_scores_gemma":[0.000008500893,0.0001843889,0.0001088912,0.0001628278,0.00007215574,0.0001352134,0.0000188029,0.0000856367,0.0003084652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007258508,"about_ca_system_score_gemma":0.00001717187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001875239,"about_ca_topic_score_gemma":0.00001713227,"domain_scores_codex":[0.9990368,0.00001093615,0.0002214052,0.0002524827,0.00009524677,0.0003831654],"domain_scores_gemma":[0.9995337,0.00002042049,0.00003068186,0.0002177858,0.0001103509,0.00008707649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000736922,0.00001091432,0.0004284646,0.00008055565,0.00005021879,0.000001268651,0.0002396156,0.00006962234,0.9295035,0.000002961406,0.03346041,0.03614512],"study_design_scores_gemma":[0.0006359501,0.00002419828,0.0005482858,0.0001835533,0.0001510711,0.0001848585,0.0001573017,0.2913603,0.6936576,0.0001477565,0.01219999,0.0007491212],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.160228,0.0004810652,0.8171665,0.0008814819,0.0003974114,0.0003021221,0.00004440564,0.0015133,0.01898574],"genre_scores_gemma":[0.9870175,0.000003906263,0.01104181,0.0001668573,0.0003779812,0.00001478691,0.0000234551,0.00006178052,0.00129193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8267896,"threshold_uncertainty_score":0.751916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004799046264895785,"score_gpt":0.2021562645984957,"score_spread":0.1973572183335999,"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."}}