{"id":"W3195552573","doi":"10.3390/app11167488","title":"Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Institute of Neurological Disorders and Stroke; National Cancer Institute; National Institutes of Health","keywords":"Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0003504057,0.00007817564,0.000189819,0.00007750613,0.000129577,0.00001987362,0.00005702512,0.00002292867,0.00007766276],"category_scores_gemma":[0.0002757077,0.00006224654,0.00001923536,0.0002836177,0.0003468296,0.00004533765,0.00003568817,0.0002209661,0.000003763038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001647201,"about_ca_system_score_gemma":0.000136374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003559246,"about_ca_topic_score_gemma":0.000007614151,"domain_scores_codex":[0.9991975,0.00003200885,0.0001695665,0.0002337074,0.0002322456,0.0001349392],"domain_scores_gemma":[0.9995755,0.0001265321,0.00009992573,0.00006611883,0.00005282562,0.00007910646],"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":[0.00006101752,0.0003031747,0.05226659,0.0001154256,0.00004019594,0.0000391534,0.001842051,0.001174485,0.8940679,0.0004765269,0.00008457699,0.04952888],"study_design_scores_gemma":[0.00280421,0.0002667758,0.01274364,0.0001177296,0.00006175256,0.0000551013,0.002504232,0.731716,0.2491945,0.0001273372,0.0002565645,0.0001521638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776196,0.0001993981,0.01888715,0.001654495,0.00006161696,0.0001362399,0.000003117785,0.0000346514,0.001403728],"genre_scores_gemma":[0.9728761,0.00002039744,0.02627811,0.0006179892,0.00002167795,0.000005298308,0.00001781701,0.000007068408,0.0001555481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7305415,"threshold_uncertainty_score":0.253834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192261891643533,"score_gpt":0.3264807489278931,"score_spread":0.3072545597635398,"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."}}