{"id":"W4353080404","doi":"10.54097/hset.v34i.5388","title":"Breast Cancer Prediction Based on the CNN Models","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"AI in cancer detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Breast cancer; Margin (machine learning); Medical diagnosis; Deep learning; Artificial intelligence; Computer science; Cancer; Residual neural network; Machine learning; Pattern recognition (psychology); Medicine; Internal medicine; Radiology","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":[0.0004375866,0.0000962317,0.00008019053,0.0009846383,0.0001843201,0.00006615718,0.0006943391,0.00007911425,0.000001368394],"category_scores_gemma":[0.00002498796,0.00006749058,0.00001063581,0.00461313,0.0002087553,0.0003306326,0.0001485683,0.0001829917,0.00002386278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001298244,"about_ca_system_score_gemma":0.00007425403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001942828,"about_ca_topic_score_gemma":0.000005154483,"domain_scores_codex":[0.9989138,0.000007603107,0.000112856,0.0003942724,0.0002607225,0.0003107433],"domain_scores_gemma":[0.9994023,0.00006731636,0.00002621304,0.0004249491,0.00004476339,0.00003444262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002203917,0.00001132307,0.000214735,0.000006906829,0.000002062031,0.000007846632,0.0001078628,0.3983913,0.005899407,0.5842027,0.0002436905,0.01090991],"study_design_scores_gemma":[0.00008529476,0.00002855628,0.002083366,0.00004180332,8.034448e-7,0.00001278482,0.00001161463,0.986492,0.006611052,0.003806407,0.0007486131,0.00007768566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4565897,0.0001805476,0.4133759,0.117457,0.004564889,0.0006412893,0.00002471246,0.005985981,0.001179976],"genre_scores_gemma":[0.998556,0.00008381873,0.001159048,0.00003412961,0.00003592598,0.0001023166,1.348532e-7,0.000005819141,0.00002276646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5881007,"threshold_uncertainty_score":0.2752186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01039749525109442,"score_gpt":0.211775385055101,"score_spread":0.2013778898040066,"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."}}