{"id":"W2076748039","doi":"10.1007/s10278-007-9004-0","title":"Content-based Retrieval of Mammograms Using Visual Features Related to Breast Density Patterns","year":2007,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Research Services, University of Calgary; Fundação de Apoio ao Ensino, Pesquisa e Assistência do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Histogram; Mammography; Image retrieval; Artificial neural network; Range (aeronautics); Computer vision; Image (mathematics); Breast cancer; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003168139,0.0006777211,0.001262474,0.006244235,0.0002926928,0.001011741,0.000565284,0.0008455761,0.002777818],"category_scores_gemma":[0.001933882,0.0002051913,0.0006615039,0.00308866,0.0002744764,0.0009043838,0.0005056638,0.0002814254,0.001653397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000390318,"about_ca_system_score_gemma":0.0004370596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002274882,"about_ca_topic_score_gemma":0.003023428,"domain_scores_codex":[0.9997532,0.0000322465,0.00002468617,0.00003571776,0.0001107339,0.00004342254],"domain_scores_gemma":[0.9993135,0.0002386018,0.00007743489,0.00006857453,0.0002559479,0.00004594533],"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":[0.002005056,0.0005259127,0.005480826,0.0008215733,0.0001848484,0.0008594755,0.000146276,0.004499676,0.2789327,0.0008975909,0.009852084,0.695794],"study_design_scores_gemma":[0.0006891009,0.002047908,0.1095231,0.0003835295,0.002006926,0.01284034,0.001164792,0.513072,0.3325195,0.006625901,0.01886983,0.0002572853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5368376,0.01393517,0.4310857,0.001084166,0.0007989589,0.0007432362,0.004985324,0.003837761,0.006692016],"genre_scores_gemma":[0.7629097,0.005306377,0.2183856,0.0003667806,0.0007934226,0.0002431299,0.006488433,0.0002090345,0.005297635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006244235,"threshold_uncertainty_score":0.009292722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211895760722835,"score_gpt":0.2919163031732813,"score_spread":0.2707267271009978,"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."}}