{"id":"W3093860814","doi":"10.1155/2020/8861035","title":"Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images","year":2020,"lang":"en","type":"article","venue":"Computational and Mathematical Methods in Medicine","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"National University Health System","keywords":"Segmentation; Prostate cancer; Prostate; Computer science; Artificial intelligence; Magnetic resonance imaging; Multimodal therapy; Deep learning; Image segmentation; Pattern recognition (psychology); Medicine; Radiology; Cancer; Surgery; Internal 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":[0.003358464,0.001573879,0.0006906738,0.001959326,0.0003520079,0.001230776,0.001166006,0.001427288,0.001947946],"category_scores_gemma":[0.006330142,0.0004424668,0.001045262,0.0009619105,0.0004438446,0.001223356,0.001036981,0.0007448256,0.0005375568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001457524,"about_ca_system_score_gemma":0.000986635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00660135,"about_ca_topic_score_gemma":0.009812351,"domain_scores_codex":[0.9989566,0.0002843966,0.00008527529,0.0003010371,0.0002638466,0.0001087873],"domain_scores_gemma":[0.9987024,0.0005565248,0.0001519884,0.0001413927,0.0003867538,0.00006086771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009195236,0.0001578161,0.005965823,0.0003544942,0.000517974,0.0002139915,0.0001462926,0.6350283,0.02017404,0.00244694,0.003261281,0.3308136],"study_design_scores_gemma":[0.00001930833,0.0001125376,0.001550741,0.00002595429,0.00004506291,0.00009019379,0.00003081687,0.9867572,0.009246592,0.000982626,0.001123722,0.00001537332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2766371,0.003505313,0.7082187,0.0006336158,0.0001691819,0.0003059879,0.001032,0.005326952,0.004171192],"genre_scores_gemma":[0.6682398,0.0007694059,0.3250363,0.000286754,0.00005114013,0.0002288137,0.001932276,0.0005742843,0.002881277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00660135,"threshold_uncertainty_score":0.01776147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1735646687131716,"score_gpt":0.4903542715781816,"score_spread":0.3167896028650101,"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."}}