{"id":"W3012525336","doi":"10.1007/s00500-020-04842-7","title":"Improving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithm","year":2020,"lang":"en","type":"article","venue":"Soft Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Thresholding; Computer science; Image segmentation; Algorithm; Entropy (arrow of time); Benchmark (surveying); Artificial intelligence; Fuzzy logic; Segmentation; Pattern recognition (psychology); Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.00032761,0.0001235146,0.0001232932,0.00002850671,0.0004070161,0.0003194122,0.0004956797,0.00002576578,0.00001440675],"category_scores_gemma":[0.00026521,0.00009951046,0.0000265697,0.0002862588,0.00007360168,0.0003989491,0.0007643838,0.0001977823,0.000004989132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002446604,"about_ca_system_score_gemma":0.00003368397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001755084,"about_ca_topic_score_gemma":4.436905e-8,"domain_scores_codex":[0.9988432,0.00006903945,0.0002322816,0.0003598083,0.0002607112,0.0002349468],"domain_scores_gemma":[0.9993287,0.0001321523,0.0001376986,0.0001941284,0.00008619936,0.0001211728],"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":[0.000002610369,0.00001950599,0.00004690154,0.00002681232,0.00001344011,0.00001448386,0.001739344,0.0007086163,0.02222128,0.0002984816,0.007599386,0.9673091],"study_design_scores_gemma":[0.0001759262,0.0000775626,0.000007290909,0.0000165261,0.000005172833,0.00001536146,0.00005901336,0.9791321,0.02010706,0.0001537184,0.0001339602,0.00011638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001429165,0.0002289558,0.9951223,0.002289209,0.000135074,0.0001915496,0.000001569179,0.0004805304,0.0001216284],"genre_scores_gemma":[0.08906303,0.00001471128,0.9071226,0.00360574,0.0001565971,0.000002631828,0.000007704946,0.0000137531,0.00001326327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9784234,"threshold_uncertainty_score":0.4057918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120368337668539,"score_gpt":0.2481261040573103,"score_spread":0.2369224206806249,"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."}}