{"id":"W4200066891","doi":"10.1016/j.media.2021.102336","title":"Head and neck tumor segmentation in PET/CT: The HECKTOR challenge","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":203,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; Université de Sherbrooke","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Segmentation; Thresholding; Artificial intelligence; Computer science; Sørensen–Dice coefficient; Modality (human–computer interaction); Leverage (statistics); Positron emission tomography; Medicine; Nuclear medicine; Medical physics; Pattern recognition (psychology); Image segmentation; Computer vision; Image (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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008039452,0.000132149,0.0004403995,0.000177154,0.00008979,0.00005205758,0.0001043451,0.00001469418,0.001645344],"category_scores_gemma":[0.001772995,0.00008838154,0.0001673589,0.0009172283,0.0002092461,0.0000732952,0.00008859398,0.0006014485,0.0000219097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004746037,"about_ca_system_score_gemma":0.0001318005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003158035,"about_ca_topic_score_gemma":0.0002822911,"domain_scores_codex":[0.998179,0.000165132,0.0003570565,0.0003475502,0.0006884378,0.0002628715],"domain_scores_gemma":[0.9990677,0.0001981572,0.00006453217,0.0002894274,0.00007002192,0.0003101988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000153823,0.001914787,0.614062,0.0005927432,0.003778685,0.1041826,0.002801018,0.00006824236,0.005461352,0.0006970323,0.00682184,0.2594659],"study_design_scores_gemma":[0.008366926,0.0002466285,0.4561817,0.0005669863,0.004851155,0.004988658,0.004218274,0.4961246,0.001195922,0.0005351261,0.02209826,0.0006257548],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315662,0.001685682,0.00222063,0.06232565,0.00007063656,0.0001114646,0.000001940773,0.00003198054,0.001985792],"genre_scores_gemma":[0.9880181,0.0009808907,0.003254615,0.006613506,0.0001948908,0.00002076658,0.00007440078,0.00001798357,0.0008248326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4960563,"threshold_uncertainty_score":0.9992673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009524723942521691,"score_gpt":0.3205935885383795,"score_spread":0.3110688645958578,"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."}}