{"id":"W2608682763","doi":"10.1109/icpr.2016.7900296","title":"A multi-objective approach based on TOPSIS to solve the image segmentation combination problem","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"TOPSIS; Segmentation; Image segmentation; Computer science; Scale-space segmentation; Segmentation-based object categorization; Artificial intelligence; Consistency (knowledge bases); Image fusion; Image (mathematics); Pattern recognition (psychology); Fusion; Enhanced Data Rates for GSM Evolution; Mathematical optimization; Data mining; Mathematics; Operations research","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":[0.006300178,0.002705748,0.002764964,0.006042413,0.001080349,0.002982718,0.002402187,0.001984463,0.002775013],"category_scores_gemma":[0.004820334,0.0008013888,0.003345105,0.004782718,0.0009825006,0.001902201,0.001909367,0.001717156,0.0004508557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001679907,"about_ca_system_score_gemma":0.002953356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006091751,"about_ca_topic_score_gemma":0.005575162,"domain_scores_codex":[0.9959528,0.001401422,0.0003400453,0.0004999091,0.001593491,0.0002123125],"domain_scores_gemma":[0.9978858,0.001070213,0.0002457498,0.00007536438,0.0006521693,0.00007075609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000131361,0.0002444867,0.001385188,0.001174826,0.0009773147,0.0002227546,0.0003725276,0.6742745,0.006915648,0.0169869,0.002294276,0.2950201],"study_design_scores_gemma":[0.00001721644,0.0001760089,0.0004766538,0.00005769533,0.0001213678,0.00006018373,0.00008827659,0.9875768,0.001191285,0.009049904,0.001146185,0.00003838301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005204154,0.0006205405,0.9920726,0.0001479896,0.00004322123,0.0001286488,0.00004464177,0.0001453347,0.001592874],"genre_scores_gemma":[0.2468199,0.0008271136,0.7497973,0.0001436086,0.00009810174,0.0005030114,0.0002087395,0.00007578618,0.001526485],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006300178,"threshold_uncertainty_score":0.03331894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01900689717070563,"score_gpt":0.2845662128028283,"score_spread":0.2655593156321227,"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."}}