{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001959471,0.0005534241,0.0006725257,0.0009068269,0.0007268992,0.001282687,0.003082898,0.0005680132,0.000003431681],"category_scores_gemma":[0.000162508,0.0004742227,0.0001136228,0.000420398,0.002856767,0.001274577,0.002709762,0.001014407,0.000001538527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002714366,"about_ca_system_score_gemma":0.001225767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000259177,"about_ca_topic_score_gemma":0.00005152825,"domain_scores_codex":[0.9947542,0.00007532293,0.0005469662,0.002221597,0.001592763,0.0008091672],"domain_scores_gemma":[0.9966986,0.0009690683,0.0003001869,0.001268423,0.0001995674,0.0005641407],"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":[0.00001100783,0.00002647941,0.0000365889,0.0001013045,0.000008874596,0.00007620126,0.0002795546,0.0007064146,0.00001313136,0.002942971,0.0005156651,0.9952818],"study_design_scores_gemma":[0.0004880324,0.000263524,0.0000986958,0.0005432664,0.000007864533,0.0003783739,6.594366e-8,0.7859921,0.0000267491,0.2099497,0.001831766,0.0004197576],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004174075,0.0007891441,0.990241,0.005255408,0.002788115,0.0005322106,0.000006732712,0.0001643209,0.0001813461],"genre_scores_gemma":[0.1000024,0.0002584047,0.8940414,0.003100409,0.002387292,0.00004933547,0.00001153079,0.00006365978,0.00008551872],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.994862,"threshold_uncertainty_score":0.9998569,"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."}}