{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003799055,0.0007117147,0.001399751,0.001329083,0.0003426583,0.001501238,0.001878622,0.000589734,0.000006030173],"category_scores_gemma":[0.00003195035,0.0006506527,0.001776262,0.003860375,0.0002025904,0.001346072,0.0001402394,0.0006411357,0.00001271401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001858929,"about_ca_system_score_gemma":0.0007419253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006878936,"about_ca_topic_score_gemma":0.000002184953,"domain_scores_codex":[0.996622,0.00008657558,0.000793454,0.001199364,0.0005937281,0.0007049178],"domain_scores_gemma":[0.9978207,0.0002615013,0.000517769,0.001032243,0.0001635682,0.0002042705],"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":[0.000004403541,0.00003807199,9.349146e-7,0.004314829,0.0001205613,0.000009252427,0.00005395332,5.031557e-7,3.171111e-8,0.0003041434,0.0008005835,0.9943528],"study_design_scores_gemma":[0.0002826047,0.0001419616,4.013278e-7,0.002785154,0.0002942479,0.0001102858,0.000001859618,0.001764988,3.078922e-7,0.002154085,0.9917439,0.0007201709],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.106842e-8,0.6715989,0.3243067,0.00001649394,0.001989857,0.000965804,0.0001445004,0.0004138164,0.0005638378],"genre_scores_gemma":[0.000001794168,0.9451864,0.05151484,0.00002154149,0.00238896,0.0005831071,0.0001201067,0.0001111445,0.0000720442],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9936326,"threshold_uncertainty_score":0.9995944,"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."}}