{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002443806,0.0001677623,0.0001554443,0.0004470085,0.0003237396,0.0002198208,0.0000989726,0.0001307428,0.000003025719],"category_scores_gemma":[0.000004552645,0.0001803398,0.00002890884,0.0006709203,0.0001178959,0.00243926,0.000007430953,0.0001417232,6.807536e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002188244,"about_ca_system_score_gemma":0.00003838694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004103799,"about_ca_topic_score_gemma":0.00004612655,"domain_scores_codex":[0.9988493,0.00008080785,0.0003815159,0.0002856893,0.0002758557,0.0001268779],"domain_scores_gemma":[0.9992141,0.0000472211,0.0002371666,0.0001871695,0.0002371318,0.00007716921],"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":[0.0001365328,0.0002661005,0.0007574953,0.0005296568,0.0000929259,7.88679e-7,0.01307815,0.01739486,0.009149151,0.4596324,0.0000285549,0.4989334],"study_design_scores_gemma":[0.0005867682,0.0001921296,0.0007832858,0.00005407744,0.00002049867,0.000017735,0.00007329341,0.9872745,0.009715537,0.0009860672,0.0001187162,0.000177447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1832773,0.00002316511,0.8157635,0.00003728817,0.0005065923,0.000278403,0.000004343549,0.0001041105,0.000005333315],"genre_scores_gemma":[0.994064,0.0003946724,0.005193719,0.0002686981,0.00003644975,0.00002008583,0.000008365041,0.00001135548,0.000002652793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9698796,"threshold_uncertainty_score":0.7354043,"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."}}