{"id":"W4327770666","doi":"10.1007/978-3-031-27420-6_23","title":"Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Deep learning; Random forest; Image segmentation; Machine learning; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008861487,0.0002523323,0.0004136395,0.0005189672,0.0002006894,0.0001174731,0.00008205625,0.00005802633,0.000005973082],"category_scores_gemma":[0.0003300121,0.0002272011,0.00002742332,0.0001909276,0.0003480835,0.0001178039,0.0001583457,0.0009940478,3.775385e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001273379,"about_ca_system_score_gemma":0.0000810437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000248682,"about_ca_topic_score_gemma":0.0002208092,"domain_scores_codex":[0.9984068,0.0000378136,0.0002994922,0.0006579466,0.0003067707,0.0002912353],"domain_scores_gemma":[0.9992029,0.0003841565,0.0001382407,0.0000943608,0.0000658046,0.0001145289],"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":[0.00005980851,0.00001349959,0.09128561,0.0004440363,0.00002452004,0.0001720595,0.0006159403,0.01040493,0.00118463,0.0001023084,0.00000464573,0.895688],"study_design_scores_gemma":[0.0008454887,0.0003299042,0.01033629,0.001207095,0.000031473,0.0002871341,0.000002395539,0.9850168,0.000156394,0.001043993,0.0005409372,0.0002021025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09327521,0.002754927,0.8984742,0.002088834,0.0007725354,0.001402052,0.0000101108,0.0008634749,0.000358622],"genre_scores_gemma":[0.7350256,0.005612025,0.255806,0.0008481282,0.0007606262,0.000109332,0.000177931,0.00018928,0.001471099],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9746119,"threshold_uncertainty_score":0.9264994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503946597461299,"score_gpt":0.3084839568747205,"score_spread":0.2934444909001075,"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."}}