{"id":"W2149663006","doi":"10.1109/icassp.2004.1326596","title":"A novel segmentation technique for carotid ultrasound images","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Segmentation; Image segmentation; Ultrasound; Radiology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003019538,0.000113285,0.0001036451,0.0001002526,0.00008642749,0.0001263112,0.0004615285,0.00005236196,0.00003217013],"category_scores_gemma":[0.0001289333,0.0001006348,0.00005654611,0.0002277577,0.00005352924,0.0006572225,0.00006201269,0.00006781468,0.00001505521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001045955,"about_ca_system_score_gemma":0.00008001515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007492171,"about_ca_topic_score_gemma":0.000005126893,"domain_scores_codex":[0.9990198,0.00001360401,0.0002213526,0.0003031319,0.0002391415,0.0002029452],"domain_scores_gemma":[0.9993079,0.0001171313,0.00007133847,0.0003017391,0.0001135135,0.00008840014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000105331,0.00006073673,0.00001488043,0.00001294578,0.000004806776,5.590226e-7,0.0001141248,0.000008131872,0.9904396,0.007532804,0.00123939,0.0005709444],"study_design_scores_gemma":[0.0005090958,0.00009975983,0.0001373725,0.00001526043,0.000003409496,0.00003826246,0.00003607172,0.000004991635,0.9915338,0.007478114,0.00001468946,0.0001291754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001565958,0.000007117548,0.9965183,0.0006926007,0.00008742927,0.00112966,0.00000721765,0.0004810943,0.001060881],"genre_scores_gemma":[0.009109409,0.000007792884,0.988574,0.001233089,0.00003614928,0.0007272938,0.00001345216,0.00001012465,0.0002887302],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009093749,"threshold_uncertainty_score":0.4103769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0180374563035573,"score_gpt":0.2988427461640752,"score_spread":0.2808052898605179,"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."}}