{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009690375,0.0008755833,0.001045944,0.003348628,0.0003119576,0.001648868,0.001158127,0.001143952,0.004338092],"category_scores_gemma":[0.001805209,0.000360747,0.001162564,0.00269233,0.0003611116,0.002325222,0.001485015,0.0006416753,0.003260521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003026509,"about_ca_system_score_gemma":0.000480934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001169699,"about_ca_topic_score_gemma":0.002742229,"domain_scores_codex":[0.9992226,0.0001082033,0.00007543182,0.0001259676,0.0004142397,0.00005347479],"domain_scores_gemma":[0.9992811,0.0002104451,0.00005961092,0.00008019737,0.0003310759,0.00003769066],"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":[0.0002903828,0.0002607233,0.000581894,0.0006557308,0.0001704895,0.0001999828,0.0001050792,0.002879347,0.1428127,0.002050829,0.01047956,0.8395132],"study_design_scores_gemma":[0.0002538524,0.001445334,0.01138377,0.0003748453,0.001931416,0.004919266,0.0006613268,0.5782042,0.2902561,0.02385557,0.08637045,0.0003439488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01454682,0.00389601,0.9716518,0.0004851054,0.0002320301,0.0002772456,0.0009118065,0.004278034,0.003721112],"genre_scores_gemma":[0.0880834,0.002284141,0.8972101,0.0004833382,0.0003779921,0.0003164397,0.002234936,0.0003861178,0.008623498],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004338092,"threshold_uncertainty_score":0.01451236,"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."}}