{"id":"W1966871760","doi":"10.3791/50341","title":"Detection of Architectural Distortion in Prior Mammograms &lt;em&gt;via&lt;/em&gt; Analysis of Oriented Patterns","year":2013,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"AI in cancer detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Distortion (music); Mammography; False positive paradox; Artificial intelligence; Pattern recognition (psychology); Breast cancer; Linear discriminant analysis; Receiver operating characteristic; Fractal dimension; Computer science; Medicine; Cancer; Mathematics; Fractal; Internal 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005173553,0.0002140491,0.0002212883,0.001291648,0.0001179395,0.0003103485,0.0002379157,0.000180636,0.001114206],"category_scores_gemma":[0.002078885,0.0001253779,0.0002097759,0.0003884546,0.0002383158,0.0002111692,0.0002668928,0.0001849952,0.0002676907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001242504,"about_ca_system_score_gemma":0.000150836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001003919,"about_ca_topic_score_gemma":0.002191271,"domain_scores_codex":[0.9997529,0.00004477093,0.00002233334,0.00005180751,0.00009685999,0.0000312643],"domain_scores_gemma":[0.9990638,0.0003559559,0.0001542616,0.0002083884,0.0001759485,0.00004170591],"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":[0.0006566102,0.00014539,0.1421417,0.0001821679,0.00009531467,0.000863281,0.000276158,0.007246398,0.2909905,0.0006985408,0.0009826617,0.5557213],"study_design_scores_gemma":[0.00003228292,0.0004676842,0.5225619,0.0000444688,0.0001645395,0.008411217,0.0002520086,0.1994434,0.2626463,0.0009574624,0.004955933,0.00006282103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8069568,0.000362258,0.1898766,0.0001012595,0.00002027001,0.00006690511,0.0002419498,0.0006534547,0.001720473],"genre_scores_gemma":[0.9118498,0.0001693568,0.08703825,0.00001879412,0.00002151463,0.00002086632,0.0002171153,0.00003129997,0.0006329343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001291648,"threshold_uncertainty_score":0.003727436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0155383715015768,"score_gpt":0.3355714869614179,"score_spread":0.3200331154598411,"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."}}