{"id":"W4392905953","doi":"10.32920/25413817.v1","title":"Improving Interactive Segmentation Techniques in Medical Imaging","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Feature (linguistics); Function (biology); Artificial intelligence; Net (polyhedron); Image segmentation; Computer vision; Pattern recognition (psychology); 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":[0.002552649,0.001803469,0.001160597,0.002161835,0.0004359704,0.002033714,0.00218806,0.002757736,0.004694211],"category_scores_gemma":[0.008355336,0.0008998485,0.001317204,0.001533658,0.0009475811,0.002718393,0.002370041,0.001777173,0.001847686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001111414,"about_ca_system_score_gemma":0.0009676951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003446623,"about_ca_topic_score_gemma":0.004987527,"domain_scores_codex":[0.9980643,0.0005183637,0.0001021874,0.000365697,0.0007863856,0.0001630519],"domain_scores_gemma":[0.9975318,0.001440646,0.0002240161,0.0003443495,0.0003444845,0.0001146169],"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":[0.0008805076,0.0002262774,0.001954375,0.0005646249,0.0002898127,0.00035755,0.0002925516,0.1388211,0.08085404,0.005549763,0.007269497,0.7629398],"study_design_scores_gemma":[0.0000488127,0.0003033575,0.00201231,0.00006032912,0.00009431083,0.0008494433,0.00006647706,0.917661,0.0582113,0.01118935,0.009441964,0.00006138058],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02238616,0.002640634,0.9671649,0.0004243112,0.00007085827,0.0001008394,0.0001668432,0.004968631,0.002076788],"genre_scores_gemma":[0.270278,0.002583361,0.7176313,0.0007898676,0.0002065321,0.0001902345,0.0008753106,0.00159703,0.00584833],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004694211,"threshold_uncertainty_score":0.01570374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007026015954259444,"score_gpt":0.3431620038575982,"score_spread":0.3361359879033387,"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."}}