{"id":"W4226337630","doi":"10.1007/978-3-030-98253-9_22","title":"Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Segmentation; Convolutional neural network; Computer science; Artificial intelligence; Proportional hazards model; Artificial neural network; Pattern recognition (psychology); Deep learning; Statistics; Mathematics","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":[0.001055045,0.0005985047,0.0005979936,0.0009272281,0.000213287,0.001069012,0.0008870084,0.0007192841,0.001558978],"category_scores_gemma":[0.001849872,0.00051984,0.001062869,0.0007039795,0.0002508075,0.0006158635,0.0005217049,0.0006327037,0.0005802891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007359016,"about_ca_system_score_gemma":0.0005669251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007764774,"about_ca_topic_score_gemma":0.009667993,"domain_scores_codex":[0.9997949,0.00005062863,0.0000155061,0.00005929024,0.0000583604,0.00002126796],"domain_scores_gemma":[0.9995945,0.0002587634,0.00003379448,0.0000287288,0.00007045692,0.00001373788],"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":[0.0004365669,0.00008878383,0.01392814,0.0001449456,0.000163921,0.0001731064,0.00007404808,0.299716,0.005965641,0.004021907,0.005370708,0.6699162],"study_design_scores_gemma":[0.00000453035,0.00003432062,0.00256185,0.00001460526,0.0000275521,0.00009637773,0.00001102933,0.9906191,0.002496983,0.00332739,0.0007931298,0.00001317261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.12807,0.005576861,0.8575737,0.0008013413,0.0002564266,0.0001139288,0.001276242,0.002148472,0.004182921],"genre_scores_gemma":[0.6364356,0.003083005,0.344191,0.0002074572,0.0001973895,0.000144894,0.001882607,0.0004142603,0.01344366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007764774,"threshold_uncertainty_score":0.01543915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008779037312565059,"score_gpt":0.2482308243721047,"score_spread":0.2394517870595396,"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."}}