{"id":"W2401364012","doi":"","title":"Supervised Machine Learning based Medical Image Annotation and Retrieval.","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Automatic image annotation; Image retrieval; Bhattacharyya distance; Artificial intelligence; Pattern recognition (psychology); Computer science; Support vector machine; Visual Word; Annotation; Pairwise comparison; Feature vector; Local binary patterns; Image (mathematics); Histogram","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":[],"consensus_categories":[],"category_scores_codex":[0.0005438774,0.00008999478,0.00009496624,0.00008556645,0.0001103265,0.0001367502,0.0003084727,0.00006962715,0.0003254871],"category_scores_gemma":[0.0002759789,0.00007355892,0.00002910478,0.0003122449,0.00005732345,0.0005134504,0.00008229561,0.0001751817,0.00005098865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002274935,"about_ca_system_score_gemma":0.00008121505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001098712,"about_ca_topic_score_gemma":0.000003296137,"domain_scores_codex":[0.998926,0.00007999637,0.0001854068,0.0002550541,0.0004050424,0.0001485262],"domain_scores_gemma":[0.999431,0.0001007031,0.00004319103,0.0001979045,0.0001051108,0.0001221046],"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":[0.00004673039,0.0002036973,0.001524346,0.00005041909,0.00001299409,0.00002173111,0.0003987568,0.000006703971,0.05902299,0.03987193,0.001437756,0.8974019],"study_design_scores_gemma":[0.0003381521,0.00005882518,0.001091822,0.000008974248,0.000002095108,0.000009331798,0.000006990018,0.8736131,0.1133093,0.0003046763,0.01113811,0.0001186383],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002166213,0.0001306853,0.9776772,0.01637748,0.00002396799,0.00008718249,5.889651e-7,0.0005835273,0.00295315],"genre_scores_gemma":[0.7291564,0.0001052627,0.267194,0.001986471,0.0000689572,0.000005191377,0.00001203523,0.0000100407,0.001461706],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8972833,"threshold_uncertainty_score":0.3563856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157922215551452,"score_gpt":0.2545008822018898,"score_spread":0.2429216600463753,"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."}}