{"id":"W4384925218","doi":"10.1002/mp.16615","title":"Information fusion for fully automated segmentation of head and neck tumors from PET and CT images","year":2023,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Head and neck; Medical imaging; Nuclear medicine; Segmentation; Positron emission tomography; Medicine; Medical physics; Image registration; Image fusion; Image segmentation; Radiology; Computer vision; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002501182,0.00007755332,0.000204737,0.00004746371,0.00005394715,0.00001535612,0.00003475858,0.00002318184,0.00002143131],"category_scores_gemma":[0.0005636715,0.00006413306,0.00002822909,0.0001335379,0.0001409864,0.0001318912,0.00004327808,0.0001412265,0.0000052265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001201619,"about_ca_system_score_gemma":0.00005375328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001092309,"about_ca_topic_score_gemma":7.598238e-7,"domain_scores_codex":[0.9992024,0.00001705715,0.0002297542,0.00009229466,0.0003348662,0.0001235922],"domain_scores_gemma":[0.9994786,0.0001692911,0.000083748,0.00007358038,0.00004682239,0.0001479771],"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":[0.0001925552,0.0001177881,0.03903272,0.00102457,0.00009229913,0.00005132222,0.002004128,0.00004335496,0.01013177,0.0002413007,0.01790217,0.929166],"study_design_scores_gemma":[0.008180133,0.0005321021,0.3099141,0.001078264,0.0001633733,0.00007539898,0.0006144631,0.6625124,0.01052237,0.004328619,0.001833244,0.0002455243],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845533,0.0000395065,0.01200555,0.002778617,0.00009264805,0.0002248213,0.00002530137,0.0001585056,0.0001216921],"genre_scores_gemma":[0.9967377,0.0001269541,0.001728171,0.0006538794,0.000145714,0.00001283272,0.0005610044,0.0000104181,0.00002338844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9289205,"threshold_uncertainty_score":0.261527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008871945584463657,"score_gpt":0.3028740297143155,"score_spread":0.2940020841298519,"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."}}