{"id":"W4392509442","doi":"10.1007/978-3-031-54806-2_13","title":"3d U-Net with ROI Segmentation of Kidneys and Masses in CT Scans","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Computer vision; Computer graphics (images)","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":[0.0009504411,0.001394313,0.001032003,0.001729732,0.0004930355,0.002169577,0.001732541,0.002241457,0.006962464],"category_scores_gemma":[0.001148958,0.001127805,0.00151531,0.001301709,0.0004271523,0.0009530793,0.001270979,0.0007375079,0.003622204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007488186,"about_ca_system_score_gemma":0.001360343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007680361,"about_ca_topic_score_gemma":0.01211761,"domain_scores_codex":[0.9996288,0.00004461739,0.00003241381,0.0001233515,0.0001179737,0.00005284095],"domain_scores_gemma":[0.9997295,0.00009609086,0.00002314238,0.00005039223,0.00007738847,0.00002359612],"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":[0.0005202996,0.000139183,0.002311798,0.0004424751,0.0001453029,0.0005451045,0.0001303839,0.1403472,0.02637922,0.005982977,0.0206706,0.8023855],"study_design_scores_gemma":[0.00002236,0.00009916254,0.001391807,0.000110723,0.00007103819,0.000854936,0.0000363908,0.9544368,0.02534768,0.005236205,0.01234996,0.00004302647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02375447,0.001959309,0.9483953,0.0003761665,0.0002597987,0.000279831,0.002181525,0.01413756,0.008655977],"genre_scores_gemma":[0.1062768,0.001139644,0.8780699,0.0003338361,0.0001188879,0.0002052694,0.002751541,0.001529407,0.009574582],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007680361,"threshold_uncertainty_score":0.02329183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008007707190731995,"score_gpt":0.2659208682364771,"score_spread":0.2579131610457451,"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."}}