{"id":"W2920834488","doi":"10.1016/j.compmedimag.2019.03.003","title":"Special issue on machine learning in medical imaging","year":2019,"lang":"en","type":"editorial","venue":"Computerized Medical Imaging and Graphics","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Encoder; Discriminative model; Ground truth; Deep learning; Similarity (geometry); Computer vision; Encoding (memory); Noise (video); Set (abstract data type); Backpropagation; Artificial neural network; Image (mathematics)","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":[0.009246689,0.005267367,0.006942966,0.0108982,0.003750853,0.01266085,0.003775498,0.01565923,0.03181227],"category_scores_gemma":[0.02750609,0.001815481,0.003589352,0.00317508,0.003102981,0.004627253,0.002688684,0.01865173,0.01892043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004707284,"about_ca_system_score_gemma":0.003628461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001970765,"about_ca_topic_score_gemma":0.008160805,"domain_scores_codex":[0.9931461,0.001383331,0.0008013593,0.0008748828,0.003411246,0.0003831455],"domain_scores_gemma":[0.9646661,0.01437469,0.002238069,0.0009918723,0.01252403,0.005205233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002415181,0.00001047882,0.00001672214,0.0001650003,0.00002300136,0.00004348732,0.00000411494,0.00003346882,0.0000364718,0.0002657636,0.9935797,0.005797643],"study_design_scores_gemma":[0.00008121516,0.000032282,0.0003007795,0.0005168859,0.00008836133,0.0002430856,0.000016013,0.000505397,0.00009923065,0.00262588,0.995465,0.00002589174],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00001475799,0.009011318,0.0002291479,0.01873162,0.9702819,0.00001389324,0.00004922943,0.00005808156,0.001609954],"genre_scores_gemma":[0.0001838035,0.003643562,0.0001324124,0.005914744,0.9823837,0.00001696425,0.0000292522,0.00004476514,0.007650833],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03181227,"threshold_uncertainty_score":0.1064227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006347021992782732,"score_gpt":0.2697452148390441,"score_spread":0.2633981928462614,"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."}}