{"id":"W1542499753","doi":"10.1109/mwscas.2003.1562307","title":"Region of Interest Identification in Prostate TRUS mages Based on Gabor Filter","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Grey level; Region of interest; Artificial intelligence; Gabor filter; Computer vision; Contrast (vision); Computer science; Pattern recognition (psychology); Feature (linguistics); Texture (cosmology); Filter (signal processing); Image texture; Feature extraction; Image (mathematics); Image segmentation","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":[0.0003693705,0.0002541675,0.0004749817,0.001295843,0.0001583332,0.0005463882,0.0002480314,0.0005169151,0.0006034232],"category_scores_gemma":[0.001019615,0.0002140226,0.0003368417,0.0004674759,0.0002189649,0.0005137214,0.0002127755,0.0002470884,0.0005268576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000214882,"about_ca_system_score_gemma":0.000246713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001218413,"about_ca_topic_score_gemma":0.001325489,"domain_scores_codex":[0.999764,0.00004472082,0.00001258616,0.00004605464,0.00009081657,0.00004170383],"domain_scores_gemma":[0.9997228,0.000121626,0.00003673897,0.00002151746,0.00007475483,0.0000226639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005627594,0.00006582989,0.003247567,0.0001222757,0.00004139772,0.0003653785,0.0001189153,0.008891732,0.5385639,0.00140484,0.001397924,0.4452175],"study_design_scores_gemma":[0.00004176407,0.0003076826,0.03005726,0.00002866488,0.0001069639,0.002019951,0.0001193566,0.6607864,0.2998171,0.001353417,0.005298199,0.00006312245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1445782,0.0009286578,0.8521881,0.0001207027,0.00005402164,0.00003530663,0.00006262927,0.001288117,0.0007442757],"genre_scores_gemma":[0.4794651,0.000599248,0.5174199,0.00007924162,0.0000475158,0.000046496,0.0001321803,0.0001672386,0.00204312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001295843,"threshold_uncertainty_score":0.00242269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03449454355969277,"score_gpt":0.2861676181601013,"score_spread":0.2516730746004085,"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."}}