{"id":"W1601871730","doi":"10.1007/11878773_76","title":"Supervised Machine Learning Based Medical Image Annotation and Retrieval in ImageCLEFmed 2005","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Bhattacharyya distance; Computer science; Image retrieval; Automatic image annotation; Artificial intelligence; Pattern recognition (psychology); Feature vector; Support vector machine; Annotation; Classifier (UML); Local binary patterns; Pairwise comparison; Semantic gap; Visual Word; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002064567,0.0004506013,0.000490917,0.001025716,0.0002003236,0.0005146549,0.001702071,0.0004173722,0.00005700027],"category_scores_gemma":[0.0004230367,0.0004166254,0.00008540382,0.0008250739,0.0008384727,0.0007384336,0.0006053746,0.001312161,0.00001338575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000288181,"about_ca_system_score_gemma":0.0008316386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007557576,"about_ca_topic_score_gemma":0.0001009838,"domain_scores_codex":[0.9958311,0.0001100545,0.0006642126,0.001336354,0.001453728,0.0006044995],"domain_scores_gemma":[0.9980869,0.0005316978,0.0002545009,0.000675925,0.0002437694,0.0002072333],"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":[0.00007538866,0.0001761174,0.001080159,0.0002114038,0.00001016995,0.0006203967,0.0005135605,0.001955624,0.007330064,0.005046022,0.00008313959,0.9828979],"study_design_scores_gemma":[0.0005626078,0.0001109937,0.001098201,0.0002835604,0.000004069477,0.0000371613,1.171086e-7,0.9700033,0.01197348,0.01467483,0.0007422956,0.0005093575],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000146165,0.0005018394,0.9946001,0.002917221,0.0002452783,0.0003594168,0.000005289485,0.0002727911,0.0009519185],"genre_scores_gemma":[0.1820734,0.0002822094,0.8137541,0.002409186,0.0004139084,0.00001686461,0.00008244436,0.00008419769,0.000883599],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9823886,"threshold_uncertainty_score":0.9998286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01080690493908015,"score_gpt":0.2441053304483944,"score_spread":0.2332984255093142,"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."}}