{"id":"W2051741004","doi":"10.1109/icip.2014.7025185","title":"A robust convergence index filter for breast cancer cell segmentation","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Calgary","funders":"Fondation pour la Recherche Médicale; University of Alberta; Alberta Cancer Foundation","keywords":"Filter (signal processing); Computer science; Clutter; Convergence (economics); Kernel (algebra); Mathematics; Pixel; Artificial intelligence; Algorithm; Computer vision; Pattern recognition (psychology); Radar","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001792672,0.0006451698,0.001299929,0.002128064,0.0005955694,0.001104478,0.001180862,0.001439382,0.00130828],"category_scores_gemma":[0.004402784,0.0004144998,0.0009843525,0.001322786,0.0006261017,0.001331894,0.001054088,0.001007804,0.0009558407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085327,"about_ca_system_score_gemma":0.001474253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004634852,"about_ca_topic_score_gemma":0.004734499,"domain_scores_codex":[0.9990391,0.0001259802,0.00005596313,0.000200162,0.0004893727,0.00008948787],"domain_scores_gemma":[0.998868,0.0003910623,0.0001150361,0.0001386549,0.0004096463,0.00007773206],"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":[0.0006234817,0.0001271567,0.002144451,0.0002215243,0.0001413214,0.0002113199,0.0001999804,0.1108962,0.129993,0.008654964,0.004303066,0.7424836],"study_design_scores_gemma":[0.00001896204,0.0001027832,0.001269672,0.00001030864,0.000040367,0.0002667985,0.00002550858,0.9465919,0.04601386,0.002216956,0.00340026,0.00004257226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006900497,0.0002375233,0.9917154,0.00005193117,0.00002408912,0.00002620609,0.00002956391,0.000670997,0.0003437812],"genre_scores_gemma":[0.1406341,0.0005447391,0.8553183,0.000163516,0.00008674611,0.0001225731,0.0003683018,0.0002566362,0.002505113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004634852,"threshold_uncertainty_score":0.009480655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226897965543316,"score_gpt":0.2746726238817621,"score_spread":0.2519828273274304,"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."}}