{"id":"W2167885192","doi":"10.1109/icme.2011.6011897","title":"Compression of 3D MRI images based on symmetry in prediction-error field","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Lossless compression; Computer science; Data compression; Compression (physics); Block (permutation group theory); Matching (statistics); Symmetry (geometry); Lossy compression; Artificial intelligence; Computer vision; Field (mathematics); Algorithm; Pattern recognition (psychology); Mathematics; Geometry","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":[0.0002799192,0.0003218162,0.0003403412,0.000621982,0.00009947595,0.0002280961,0.0003072538,0.0002584888,0.0009194051],"category_scores_gemma":[0.001109252,0.0001051607,0.000229561,0.0006847809,0.0002407138,0.0005003931,0.0002754663,0.0002643908,0.0003025987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001370161,"about_ca_system_score_gemma":0.0002282889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005380523,"about_ca_topic_score_gemma":0.0005522196,"domain_scores_codex":[0.9998299,0.00003246266,0.00001151457,0.00001372758,0.000100989,0.0000113481],"domain_scores_gemma":[0.9995933,0.0001943353,0.00005540631,0.00007917076,0.00006616743,0.00001167793],"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":[0.0006792497,0.0001158714,0.001290315,0.0001900755,0.00004636763,0.0006046185,0.0001275339,0.09241697,0.3443476,0.01640984,0.002591551,0.5411799],"study_design_scores_gemma":[0.00004758269,0.0002597765,0.002773619,0.00002210984,0.00002646535,0.001379178,0.00002634935,0.7894272,0.196816,0.005714592,0.003474264,0.00003284751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1342199,0.000966863,0.8616837,0.0002007678,0.0001072609,0.00008290927,0.000191273,0.0007641836,0.001783122],"genre_scores_gemma":[0.6263201,0.001322801,0.3690585,0.0001005509,0.00009490659,0.00008770054,0.0005637702,0.00008109651,0.002370643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009194051,"threshold_uncertainty_score":0.003075719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536617480719113,"score_gpt":0.2792673284903393,"score_spread":0.2539011536831481,"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."}}