{"id":"W2147364566","doi":"10.1016/j.engappai.2006.01.011","title":"Automatic clinical image segmentation using pathological modeling, PCA and SVM","year":2006,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Support vector machine; Segmentation; Pattern recognition (psychology); Image segmentation; Principal component analysis; Feature extraction; Classifier (UML); Image processing; Computer vision; 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":[0.0005085915,0.0001156393,0.0001689262,0.0001297828,0.00006736097,0.00007746398,0.0003620541,0.00007312869,0.00001661428],"category_scores_gemma":[0.0001034896,0.0001177902,0.00004808673,0.000366433,0.0001081696,0.0002787028,0.000114536,0.000135226,0.00001264896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002756916,"about_ca_system_score_gemma":0.00002672606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004669564,"about_ca_topic_score_gemma":0.000001179103,"domain_scores_codex":[0.9985564,0.00003736005,0.0007242187,0.0003130511,0.0002103414,0.0001586789],"domain_scores_gemma":[0.9991979,0.0001452786,0.0001319246,0.0003433744,0.0001119919,0.00006951327],"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":[0.000002714863,0.0003388239,0.0001773768,0.00008347254,0.00001391195,0.000005038277,0.0002039186,0.0407443,0.1511227,0.08213652,0.00004843105,0.7251228],"study_design_scores_gemma":[0.00001461268,0.0000276514,0.00009348174,0.00001684892,0.000007120012,0.000006402639,0.00002495442,0.9194192,0.07130586,0.00897133,0.000007667921,0.0001049223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05552685,0.00006520413,0.9436411,0.00006624401,0.00005009025,0.0003124892,0.000002318022,0.0003050201,0.0000306566],"genre_scores_gemma":[0.3801775,0.00001439248,0.6196766,0.00001712477,0.00004760306,0.00005397416,0.000003643541,0.000006175066,0.000002937655],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8786749,"threshold_uncertainty_score":0.4803346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04819741666510396,"score_gpt":0.3513870900051519,"score_spread":0.3031896733400479,"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."}}