{"id":"W4406264582","doi":"10.1109/euvip61797.2024.10772785","title":"Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images","year":2024,"lang":"en","type":"article","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; BC Cancer Foundation","keywords":"Computer science; Segmentation; Pipeline (software); Artificial intelligence; Image segmentation; Scintigraphy; Computer vision; Pattern recognition (psychology); Radiology; Medicine","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":[0.0003290334,0.00009744459,0.0001914956,0.0001660656,0.00004121457,0.00005813279,0.00004717539,0.00005698175,0.0000173078],"category_scores_gemma":[0.0001891559,0.00007269756,0.00005491342,0.0001324313,0.0001283416,0.0001595834,0.00001520454,0.0001127602,0.000002230798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001475748,"about_ca_system_score_gemma":0.00003521805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006599969,"about_ca_topic_score_gemma":0.000002854568,"domain_scores_codex":[0.9992257,0.00003135246,0.0002509762,0.0002650264,0.0001177795,0.0001092144],"domain_scores_gemma":[0.9994881,0.0001677364,0.00005303375,0.0001453618,0.00007895693,0.00006677001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006763203,0.00005251801,0.004889401,0.0001520615,0.00004483106,0.000005816592,0.0004009089,0.00001398133,0.9090831,0.0007604702,0.009514288,0.07501496],"study_design_scores_gemma":[0.001037247,0.0002780565,0.06856334,0.0001338079,0.0001747209,0.0000198379,0.0008700169,0.8918319,0.03438465,0.001853497,0.0007368859,0.0001160048],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8755233,0.002199687,0.1171473,0.003400201,0.0001804241,0.0003709275,0.00005051499,0.0006415462,0.0004860699],"genre_scores_gemma":[0.9400167,0.0003772985,0.05880005,0.0002213052,0.0001149846,0.00002117985,0.0002890095,0.00001954948,0.0001399597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.891818,"threshold_uncertainty_score":0.296452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01872848475131374,"score_gpt":0.3413704217057034,"score_spread":0.3226419369543897,"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."}}