{"id":"W2725817408","doi":"10.1007/978-3-319-61188-4","title":"Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging","year":2017,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Medical imaging; Graphical model; Computer graphics (images); Artificial intelligence; Bayesian probability; Computer vision; Human–computer interaction; Data science","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":[0.000933168,0.001231705,0.001392255,0.00192726,0.0002445814,0.001946214,0.001529808,0.001897025,0.009907573],"category_scores_gemma":[0.003576605,0.0008176246,0.001208358,0.002807334,0.001176505,0.002440361,0.001091728,0.002594027,0.005034697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008840077,"about_ca_system_score_gemma":0.0007250292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002284128,"about_ca_topic_score_gemma":0.002579174,"domain_scores_codex":[0.9993637,0.0001881876,0.00003319866,0.0001135194,0.0002730455,0.000028293],"domain_scores_gemma":[0.9988204,0.0007381309,0.0000643086,0.0001477276,0.0001970705,0.00003234639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003691415,0.00005209847,0.0002274981,0.001000361,0.0001212135,0.000104921,0.00008040969,0.0617181,0.001966338,0.3728585,0.09793263,0.463901],"study_design_scores_gemma":[0.00001028325,0.0000221949,0.0004158498,0.0001501339,0.00004097712,0.0003438372,0.0000221578,0.1862528,0.0007260741,0.7234401,0.08853132,0.00004431841],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0007552312,0.04812571,0.9324622,0.002164559,0.001046878,0.00002921969,0.0003516884,0.0007239683,0.01434045],"genre_scores_gemma":[0.07977416,0.09837706,0.727563,0.001805887,0.005013089,0.0002454546,0.00170242,0.000812151,0.08470675],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.009907573,"threshold_uncertainty_score":0.03314412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01000302909609496,"score_gpt":0.2729449107803261,"score_spread":0.2629418816842311,"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."}}