{"id":"W2119922429","doi":"10.1109/titb.2011.2164259","title":"Nonlinear Unsharp Masking for Mammogram Enhancement","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":199,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Calgary","keywords":"Unsharp masking; Computer science; Masking (illustration); Artificial intelligence; Nonlinear system; Flexibility (engineering); Measure (data warehouse); A priori and a posteriori; Image (mathematics); Image enhancement; Computer vision; Visualization; Pattern recognition (psychology); Mathematics; Data mining; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005624502,0.0005542392,0.0003393639,0.0004279687,0.0002465754,0.0003549655,0.0004711953,0.000434102,0.001878894],"category_scores_gemma":[0.001202856,0.0001887564,0.0003308491,0.0003107752,0.0003887574,0.0005765936,0.0005947712,0.0004753359,0.0006012411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002263176,"about_ca_system_score_gemma":0.0002161888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002167696,"about_ca_topic_score_gemma":0.0005297384,"domain_scores_codex":[0.9997517,0.00006563907,0.00001417253,0.00003675777,0.000114023,0.00001765388],"domain_scores_gemma":[0.9995475,0.0002295008,0.00005559383,0.00007740672,0.0000699771,0.00001995672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005061884,0.00004359951,0.0007008711,0.0004709967,0.00004597505,0.0002488663,0.000131047,0.0131919,0.6277289,0.01389715,0.001149919,0.3418846],"study_design_scores_gemma":[0.00003473531,0.0004167297,0.002339846,0.00008044944,0.0001138972,0.001781084,0.0000483239,0.3227853,0.640056,0.005888025,0.02638333,0.00007225926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02990784,0.001681507,0.9644575,0.0001341455,0.0001092235,0.00007311024,0.00003381278,0.0004599584,0.003142931],"genre_scores_gemma":[0.2659735,0.001832401,0.7277941,0.0001492579,0.000114939,0.00008740243,0.00008046992,0.0000952394,0.003872609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001878894,"threshold_uncertainty_score":0.006285489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792644339957853,"score_gpt":0.2594391597498786,"score_spread":0.2415127163503001,"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."}}