{"id":"W2144045556","doi":"10.1148/radiographics.20.5.g00se311479","title":"Image Processing Algorithms for Digital Mammography: A Pictorial Essay","year":2000,"lang":"en","type":"review","venue":"Radiographics","topic":"AI in cancer detection","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Cancer Institute","keywords":"Visibility; Digital mammography; Medicine; Computer vision; Artificial intelligence; Histogram equalization; Mammography; Adaptive histogram equalization; Contrast (vision); Unsharp masking; Image processing; Histogram; Computer science; Enhanced Data Rates for GSM Evolution; Image (mathematics); Breast cancer; Optics","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":[0.0008993791,0.00140426,0.0007154247,0.002730706,0.0003451018,0.001629856,0.0010653,0.001591702,0.009273475],"category_scores_gemma":[0.002226875,0.0005334273,0.0005791638,0.003180961,0.001247612,0.002720005,0.0006084174,0.003119176,0.01467505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006165795,"about_ca_system_score_gemma":0.0006003541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008016752,"about_ca_topic_score_gemma":0.0006653899,"domain_scores_codex":[0.9995449,0.00006688672,0.00006296935,0.00006670193,0.0002410025,0.00001752622],"domain_scores_gemma":[0.9984418,0.0006870301,0.00006546728,0.0001316749,0.0006141092,0.00005996316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005988018,0.0001086373,0.0002900549,0.002696863,0.00004413014,0.0001629731,0.0001322644,0.001151726,0.003610447,0.02149412,0.2776096,0.6926392],"study_design_scores_gemma":[0.000006205391,0.00003851077,0.0005997363,0.0006345914,0.00001186004,0.0008455601,0.00002599457,0.0005935813,0.000519597,0.009230373,0.9874756,0.0000183651],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006559967,0.8990619,0.04781457,0.006571062,0.01050053,0.0001069918,0.0002663682,0.0004340596,0.03458853],"genre_scores_gemma":[0.005326875,0.8670401,0.06722315,0.004425605,0.01224486,0.0001757227,0.0006819166,0.00025063,0.04263109],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009273475,"threshold_uncertainty_score":0.03102285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03435844433761451,"score_gpt":0.31601471892358,"score_spread":0.2816562745859655,"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."}}