{"id":"W2170854835","doi":"10.1109/tvcg.2010.152","title":"Exploration and Visualization of Segmentation Uncertainty using Shape and Appearance Prior Information","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"AI in cancer detection","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Visualization; Probabilistic logic; Artificial intelligence; Segmentation; Population; Context (archaeology); Data visualization; Information visualization; Set (abstract data type); Data mining; Image segmentation; Interactive visualization; Pattern recognition (psychology); Machine learning","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":[0.002693007,0.001000587,0.0008592292,0.002740696,0.0004236589,0.002384207,0.001320973,0.001116788,0.006026666],"category_scores_gemma":[0.009652933,0.0008190192,0.001068625,0.000952389,0.000590903,0.001907216,0.003106394,0.001629449,0.000816535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004249776,"about_ca_system_score_gemma":0.0007012856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009437262,"about_ca_topic_score_gemma":0.001501002,"domain_scores_codex":[0.9992638,0.0001890792,0.00005777836,0.0001130825,0.0003273936,0.00004890118],"domain_scores_gemma":[0.995385,0.003340455,0.0003046941,0.0005017612,0.0002937598,0.0001742221],"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":[0.0009706097,0.0002032075,0.005010112,0.0005914298,0.0001714601,0.0009785917,0.002313577,0.1306305,0.0935896,0.02155076,0.01743791,0.7265522],"study_design_scores_gemma":[0.0000687669,0.0001589822,0.003287814,0.0001370837,0.0000479728,0.0008643137,0.000191268,0.9060195,0.04366605,0.02892209,0.01648711,0.0001489955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01078555,0.0001338717,0.9780942,0.0003279676,0.00001544237,0.00003485746,0.0002631105,0.009777801,0.0005671454],"genre_scores_gemma":[0.1651995,0.0003551691,0.8307509,0.0002132866,0.0000590294,0.0001850304,0.0006169781,0.001950203,0.0006698697],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006026666,"threshold_uncertainty_score":0.02016115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02166560418839918,"score_gpt":0.2851812200151602,"score_spread":0.263515615826761,"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."}}