{"id":"W4401357274","doi":"10.3390/bioengineering11080796","title":"Interactive Cascaded Network for Prostate Cancer Segmentation from Multimodality MRI with Automated Quality Assessment","year":2024,"lang":"en","type":"article","venue":"Bioengineering","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Western University","funders":"City University of Hong Kong","keywords":"Segmentation; Computer science; Artificial intelligence; Workflow; Scale-space segmentation; Robustness (evolution); Image segmentation; Computer vision; Annotation; Pattern recognition (psychology); Database","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":[0.001501315,0.001376687,0.0009238366,0.001250409,0.0005775493,0.0009880898,0.002068941,0.001498681,0.003219226],"category_scores_gemma":[0.003321162,0.000791452,0.00113013,0.0007956659,0.000564921,0.00112025,0.001635714,0.001224554,0.0008564729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001521705,"about_ca_system_score_gemma":0.001170294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01150471,"about_ca_topic_score_gemma":0.02210357,"domain_scores_codex":[0.9991592,0.0001657754,0.00003527518,0.0002642857,0.0002654905,0.0001099356],"domain_scores_gemma":[0.9992751,0.0002780514,0.00009283928,0.0001211854,0.0001811404,0.00005167447],"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":[0.0009296989,0.0002463707,0.004198441,0.0002508024,0.0002591626,0.0005112966,0.0003161692,0.4933641,0.04496459,0.005800921,0.00809541,0.4410631],"study_design_scores_gemma":[0.000009420473,0.00004405661,0.0004703831,0.000008965945,0.00002788517,0.00007823254,0.000008533884,0.9903414,0.005932705,0.002095072,0.0009697647,0.00001362479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02690318,0.0004537395,0.9655608,0.0002387535,0.00004945278,0.0001186051,0.0002522001,0.00469579,0.001727403],"genre_scores_gemma":[0.4564696,0.0005570211,0.5330505,0.0004079101,0.0001064807,0.0003691798,0.001552817,0.0009295806,0.006556873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01150471,"threshold_uncertainty_score":0.02287549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02705573969478231,"score_gpt":0.3630574206513576,"score_spread":0.3360016809565753,"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."}}