{"id":"W4401981071","doi":"10.18280/mmep.110826","title":"Simulating the Tumor Mass Changes in PET and PET/CT Segmented Images Using Unsupervised Artificial Neural Network, HSOFM","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Computer science; Pet imaging; Positron emission tomography; Nuclear medicine; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004182642,0.0001615178,0.0002430558,0.00006674822,0.00007242408,0.0001090134,0.00005123811,0.00001098587,0.00001141832],"category_scores_gemma":[0.00004106892,0.0001110436,0.00003276789,0.0001929559,0.00005675837,0.000043951,0.00004372274,0.0003356574,0.000001251932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002029427,"about_ca_system_score_gemma":0.00001071911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000166535,"about_ca_topic_score_gemma":5.527808e-7,"domain_scores_codex":[0.9990417,0.00001421057,0.0002701246,0.0002390506,0.0001441996,0.0002907546],"domain_scores_gemma":[0.9994856,0.000232505,0.00001998653,0.0001425926,0.00001615919,0.0001032122],"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":[0.000008392587,0.00005329524,0.00006208568,0.002804554,0.00003988854,0.0002842274,0.0003478534,0.9721946,0.01452978,0.008348498,0.00005688946,0.00127],"study_design_scores_gemma":[0.0001045719,0.000024001,0.000003714186,0.001377313,0.00005659685,0.0004985015,0.00003128327,0.9898596,0.0002528038,0.007555069,0.0001195971,0.0001169917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4559322,0.0006298634,0.5398149,0.002802427,0.00003751076,0.0004394405,0.00000277289,0.0002907763,0.00005011691],"genre_scores_gemma":[0.894277,0.00006166825,0.1053589,0.00006819389,0.0001109742,0.00005466759,0.000003962072,0.00003450691,0.00003016467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4383448,"threshold_uncertainty_score":0.4528226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04759536752476073,"score_gpt":0.2798972377107449,"score_spread":0.2323018701859841,"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."}}