{"id":"W2616891469","doi":"10.1007/978-3-319-59876-5_12","title":"Fully Deep Convolutional Neural Networks for Segmentation of the Prostate Gland in Diffusion-Weighted MR Images","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Waterloo; University of Toronto","funders":"","keywords":"Segmentation; Computer science; Convolutional neural network; Prostate cancer; Preprocessor; Artificial intelligence; Prostate; Prostate gland; Magnetic resonance imaging; Pattern recognition (psychology); Image segmentation; Sørensen–Dice coefficient; Artificial neural network; Computer vision; Cancer; Medicine; Radiology; 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":[0.0002927054,0.0009959447,0.0005282414,0.0004826594,0.0001492391,0.0006087399,0.0008368008,0.001010031,0.003899054],"category_scores_gemma":[0.0006223022,0.0005996781,0.0007537485,0.0005683554,0.0002297528,0.000629311,0.0006595586,0.001112762,0.001706241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006557595,"about_ca_system_score_gemma":0.0007225206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007555378,"about_ca_topic_score_gemma":0.01623399,"domain_scores_codex":[0.9999161,0.0000092426,0.00000468811,0.00002381327,0.00003012479,0.00001600743],"domain_scores_gemma":[0.9998674,0.00006172211,0.00001251284,0.00001587456,0.00003353578,0.000008934568],"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":[0.000144762,0.00005856561,0.000454487,0.000431791,0.000114296,0.000173386,0.00007321532,0.2017419,0.03469498,0.01223424,0.02001309,0.7298653],"study_design_scores_gemma":[0.000009582118,0.00003986424,0.0007473469,0.00007890342,0.0000517483,0.0002595808,0.00001182638,0.9506196,0.02079969,0.01293352,0.01442337,0.00002507013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01521924,0.01092303,0.9618399,0.0006772395,0.0002247392,0.00004942658,0.0009765323,0.0030988,0.00699104],"genre_scores_gemma":[0.2452356,0.0165831,0.6885359,0.0004645724,0.0003345376,0.0001219034,0.003821407,0.001293093,0.04360995],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007555378,"threshold_uncertainty_score":0.01502275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299005107578772,"score_gpt":0.2620556107359313,"score_spread":0.2490655596601436,"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."}}