{"id":"W2136265800","doi":"10.1109/titb.2012.2189439","title":"Semantic Image Retrieval in Magnetic Resonance Brain Volumes","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Aligarh Muslim University","keywords":"Computer science; Artificial intelligence; Support vector machine; Robustness (evolution); Pattern recognition (psychology); Image retrieval; Modalities; Image (mathematics); Information retrieval","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":[0.001015006,0.0006019245,0.0009911696,0.004014161,0.0003473175,0.001114946,0.0007891373,0.000932679,0.001904498],"category_scores_gemma":[0.00405553,0.0002068885,0.0008680931,0.002743276,0.0005127574,0.002174149,0.001017501,0.0004385409,0.001088889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005445952,"about_ca_system_score_gemma":0.000519126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0014567,"about_ca_topic_score_gemma":0.001464314,"domain_scores_codex":[0.9990937,0.0002335984,0.00007813629,0.000168967,0.0003425093,0.00008311],"domain_scores_gemma":[0.9991676,0.0002722263,0.0001302633,0.0001852849,0.000218024,0.00002668982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008624639,0.0001829171,0.001637949,0.0005458948,0.0001165476,0.0004725975,0.0003229692,0.02944103,0.1532403,0.01205489,0.007502931,0.7936194],"study_design_scores_gemma":[0.0001533674,0.0008905504,0.01318964,0.0001313642,0.0002247952,0.003881397,0.0007982728,0.7071777,0.2021388,0.04964714,0.02162025,0.0001468731],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1229751,0.004686876,0.8635325,0.00044694,0.0001761359,0.0002728457,0.0006638399,0.003221622,0.00402411],"genre_scores_gemma":[0.5625203,0.002414788,0.4293862,0.0002271137,0.0003145398,0.0001996941,0.001630667,0.000204355,0.003102311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004014161,"threshold_uncertainty_score":0.00637114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007994592828460484,"score_gpt":0.2463607866203547,"score_spread":0.2383661937918942,"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."}}