{"id":"W4300177118","doi":"10.1007/978-3-030-00928-1","title":"Medical Image Computing and Computer Assisted Intervention – MICCAI 2018","year":2018,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Image quality; Artificial intelligence; Computer vision; Medical physics; Medical imaging; Image processing; Image (mathematics); Data science; Medicine","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":[0.0009511246,0.001716075,0.001443145,0.002547886,0.0004543571,0.002326397,0.001435393,0.001245889,0.04075987],"category_scores_gemma":[0.001982184,0.0007398733,0.0006809482,0.002525401,0.0009021105,0.002815488,0.001924723,0.00274058,0.02161865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000945561,"about_ca_system_score_gemma":0.001112373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001316828,"about_ca_topic_score_gemma":0.00185166,"domain_scores_codex":[0.9992902,0.00006499789,0.00003929027,0.0001637202,0.0004045616,0.00003711904],"domain_scores_gemma":[0.9989566,0.0002431986,0.0000500568,0.0001824843,0.0004114171,0.0001563307],"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":[0.00005692164,0.00004089649,0.0001016312,0.0004463123,0.0000353957,0.00004221352,0.00002760982,0.002779503,0.002739196,0.01866253,0.3958394,0.5792284],"study_design_scores_gemma":[0.00001549937,0.00007876366,0.001570897,0.0003096178,0.0000369459,0.0008925471,0.00003050216,0.02577565,0.003671163,0.03560894,0.931953,0.00005632669],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.005748117,0.2259945,0.3703848,0.01040703,0.05503346,0.0002983468,0.002004531,0.005621167,0.324508],"genre_scores_gemma":[0.03280177,0.1186738,0.1460715,0.001253662,0.02497848,0.000350299,0.003133162,0.002568682,0.6701687],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04075987,"threshold_uncertainty_score":0.1363553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00976282647887128,"score_gpt":0.3047089176856955,"score_spread":0.2949460912068242,"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."}}