{"id":"W4307987954","doi":"10.1111/exsy.13176","title":"Enhancement of clustering techniques by coupling clustering tree and neural network: Application to brain tumour segmentation","year":2022,"lang":"en","type":"article","venue":"Expert Systems","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"European Research Consortium for Informatics and Mathematics","keywords":"Cluster analysis; Computer science; Pattern recognition (psychology); Artificial intelligence; Correlation clustering; Segmentation; Artificial neural network; CURE data clustering algorithm; Data mining; Tree (set theory); Canopy clustering algorithm; 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":[0.0003806211,0.0001112731,0.0001444601,0.0000761621,0.0003187543,0.00005237262,0.0001427015,0.00002333221,0.00001295464],"category_scores_gemma":[0.00002990793,0.0001227029,0.00002354831,0.0002620584,0.00002145549,0.000106059,0.0001378252,0.00008830563,0.000001904827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001528783,"about_ca_system_score_gemma":0.000008396738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001238288,"about_ca_topic_score_gemma":0.00001957248,"domain_scores_codex":[0.9986997,0.0001173268,0.0003515929,0.0003609406,0.0002892568,0.0001812238],"domain_scores_gemma":[0.9994558,0.00008391551,0.0002051611,0.0001777117,0.00001900729,0.0000583988],"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":[0.00003187715,0.00002318741,0.00002898395,0.00002531958,0.00000154101,4.552933e-7,0.0006876368,0.007770314,0.9821234,0.00005041944,0.001625637,0.007631237],"study_design_scores_gemma":[0.0001633616,0.0001429018,0.00004554561,0.00002234173,0.00000174041,0.00002675159,0.001173763,0.4944861,0.4967124,0.000004122591,0.007091947,0.000129033],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.286856,0.0003299659,0.7078258,0.001437204,0.0008475841,0.001915753,0.00001414873,0.0002728109,0.0005007455],"genre_scores_gemma":[0.9974054,0.00001282912,0.0002615652,0.0006346387,0.0001150399,0.001232548,0.000008519531,0.00002057785,0.0003089295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7105494,"threshold_uncertainty_score":0.5003679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02701564322791943,"score_gpt":0.287113955424667,"score_spread":0.2600983121967476,"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."}}