{"id":"W112603785","doi":"10.1007/978-3-642-15751-6_23","title":"An Integrated Approach for Medical Image Retrieval through Combining Textual and Visual Features","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Image retrieval; Information retrieval; Artificial intelligence; Computer vision; Image (mathematics)","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":[0.001633225,0.0005585375,0.0005947616,0.0004340001,0.0004320667,0.001050115,0.002912099,0.0008791367,0.00001679875],"category_scores_gemma":[0.0004181817,0.0004546709,0.0001151793,0.0005795304,0.001888202,0.001086444,0.0007826565,0.001745275,0.000003111983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144137,"about_ca_system_score_gemma":0.0008004936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000193372,"about_ca_topic_score_gemma":0.00001195726,"domain_scores_codex":[0.9957566,0.00005752659,0.0005522553,0.001738088,0.001278761,0.000616789],"domain_scores_gemma":[0.9975207,0.0005306517,0.0002864375,0.0008696768,0.0005026204,0.0002898692],"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":[0.00007600682,0.000202778,0.00001295093,0.0001196937,0.00002274818,0.00004845777,0.001306206,0.00001764466,0.01565699,0.06784733,0.00004011879,0.9146491],"study_design_scores_gemma":[0.0009731672,0.001326505,0.0001055046,0.0003482122,0.00002409149,0.0004301397,0.000004077527,0.7338712,0.1438824,0.1141595,0.003291912,0.001583334],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004604224,0.0002223747,0.9965518,0.0006446698,0.0004974926,0.0006285848,0.00001218881,0.0004002696,0.0009965894],"genre_scores_gemma":[0.03423654,0.00005854767,0.9637801,0.001248263,0.00040326,0.00001580254,0.00004981758,0.00004183593,0.0001658105],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9130657,"threshold_uncertainty_score":0.9999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01616550943508944,"score_gpt":0.2948507005168379,"score_spread":0.2786851910817484,"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."}}