{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01093774,0.001878425,0.00178961,0.002141135,0.001416469,0.002493263,0.002717197,0.004195836,0.002715703],"category_scores_gemma":[0.0236371,0.0005914245,0.001798267,0.0009667258,0.001130958,0.001319872,0.003891666,0.002429648,0.002028237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001323635,"about_ca_system_score_gemma":0.002349907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008916886,"about_ca_topic_score_gemma":0.01857924,"domain_scores_codex":[0.9918351,0.003118793,0.0005313309,0.001833399,0.002101705,0.0005796475],"domain_scores_gemma":[0.9850524,0.007089813,0.0006117752,0.002104069,0.003747727,0.001394182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003587561,0.001734343,0.0211241,0.004173484,0.001801457,0.003347289,0.002185735,0.04143563,0.04568768,0.002623443,0.2176364,0.654663],"study_design_scores_gemma":[0.001026748,0.005132372,0.1317063,0.001463562,0.001429054,0.02098657,0.006926747,0.3961039,0.1330328,0.01262532,0.2886463,0.0009205522],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7476503,0.02286353,0.1642529,0.01119228,0.008652983,0.003730278,0.0179763,0.008577608,0.01510384],"genre_scores_gemma":[0.620056,0.003989119,0.2845088,0.003353785,0.003216064,0.001516658,0.05707145,0.002887997,0.02340016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01093774,"threshold_uncertainty_score":0.05784494,"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."}}