{"id":"W2997748230","doi":"","title":"Challenges with Machine Learning for Microwave Breast Tumor detection","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Breast tumor; Microwave; Microwave imaging; Artificial intelligence; Breast tissue; Modality (human–computer interaction); Breast cancer; Computer science; Breast MRI; Machine learning; Mammography; Medical physics; Medicine; Telecommunications; Internal medicine; Cancer","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003478169,0.0001178799,0.0002423559,0.0002134195,0.00005900541,0.0001138895,0.00005533349,0.00001446389,0.000002725228],"category_scores_gemma":[0.000007460822,0.00008971996,0.00006692408,0.00006904492,0.00001578382,0.0001747451,0.000007937281,0.0001501086,0.000004229119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003659867,"about_ca_system_score_gemma":0.0000121405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005529026,"about_ca_topic_score_gemma":0.000001168941,"domain_scores_codex":[0.9992869,0.00003643033,0.0002950422,0.00009173944,0.0001755131,0.0001143809],"domain_scores_gemma":[0.9994283,0.0001080086,0.0001586622,0.00005077232,0.000196428,0.00005778327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008132665,0.00002228174,0.003873055,0.0004949621,0.0001893309,0.000009321553,0.0004199152,0.8984682,0.0346519,0.00004306686,0.0001925501,0.06155412],"study_design_scores_gemma":[0.0007992759,0.0001106296,0.00574197,0.0003777903,0.00003870688,0.003143787,0.0003861409,0.9864078,0.0003143462,0.00007026199,0.002466465,0.0001428003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5313001,0.008108753,0.4590129,0.0004624233,0.0005425175,0.0001536711,0.000005626881,0.00007713035,0.0003369248],"genre_scores_gemma":[0.9983029,0.00005693802,0.001438601,0.00001873064,0.0001089844,0.000001415308,0.000003664789,0.00002350984,0.00004526313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4670028,"threshold_uncertainty_score":0.3658674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005721709867167127,"score_gpt":0.2116527367988999,"score_spread":0.2059310269317327,"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."}}