{"id":"W2753119246","doi":"10.1007/978-3-319-66182-7_90","title":"Combining Spatial and Non-spatial Dictionary Learning for Automated Labeling of Intra-ventricular Hemorrhage in Neonatal Brain MRI","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing; Pattern recognition (psychology)","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":[0.0009274862,0.0006420999,0.0007989294,0.0011036,0.000272206,0.00104184,0.0009538497,0.0009536903,0.001172782],"category_scores_gemma":[0.001657768,0.0003728456,0.0008776819,0.001200483,0.0003678272,0.001023197,0.001178165,0.0007429831,0.001038603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002832794,"about_ca_system_score_gemma":0.000693168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002572156,"about_ca_topic_score_gemma":0.006734842,"domain_scores_codex":[0.9996239,0.00009002789,0.00002984016,0.00009681333,0.0001074131,0.00005207538],"domain_scores_gemma":[0.9993075,0.0002697631,0.00006381474,0.0001029582,0.0002179268,0.00003798235],"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":[0.0001761465,0.00008713998,0.002234864,0.0002376881,0.0001228137,0.00009157133,0.00009342712,0.03983236,0.05208139,0.002194184,0.0033344,0.899514],"study_design_scores_gemma":[0.00001463191,0.0001355697,0.002165201,0.0000409319,0.00009401905,0.0005349925,0.00009716491,0.9574889,0.03118154,0.005189402,0.003029549,0.00002815116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03584333,0.001549881,0.9593676,0.0002070405,0.0000726001,0.00006266875,0.0002476466,0.0009947442,0.001654575],"genre_scores_gemma":[0.216786,0.002058785,0.7755142,0.0002012513,0.0001164166,0.00008252313,0.001109967,0.0003636545,0.003767173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002572156,"threshold_uncertainty_score":0.005114436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009544921901032342,"score_gpt":0.2640576360688589,"score_spread":0.2545127141678266,"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."}}