{"id":"W3021454079","doi":"10.1007/s11548-020-02158-3","title":"Two-stage ultrasound image segmentation using U-Net and test time augmentation","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"AI in cancer detection","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nuance Communications (Canada); Concordia University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Sørensen–Dice coefficient; Breast ultrasound; Computer science; Stage (stratigraphy); Artificial intelligence; Ultrasound; Image segmentation; Dice; Pattern recognition (psychology); Computer vision; Radiology; Medicine; Mammography; Breast cancer; Mathematics; Cancer","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005679178,0.0009075709,0.001017209,0.0012969,0.0005856086,0.001111399,0.001216684,0.001190924,0.00396503],"category_scores_gemma":[0.00118862,0.0005470411,0.0008604656,0.001099999,0.0003547626,0.0009058801,0.001031249,0.0006076348,0.001051903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004336443,"about_ca_system_score_gemma":0.001551442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006723499,"about_ca_topic_score_gemma":0.01059198,"domain_scores_codex":[0.9996293,0.00003526913,0.00002918918,0.00009860327,0.0001348372,0.00007272172],"domain_scores_gemma":[0.9995182,0.0001449726,0.00004085281,0.00009740047,0.0001612312,0.00003743855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001151053,0.0002426436,0.003091339,0.0001698658,0.00008239493,0.0002452159,0.0001308445,0.04804251,0.09002496,0.002004871,0.00222947,0.8525848],"study_design_scores_gemma":[0.00002199618,0.0002302988,0.002674131,0.00001532411,0.00005894084,0.0002792863,0.000044782,0.935617,0.05731869,0.001139409,0.002569207,0.00003097495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08736811,0.0003566338,0.9026503,0.0001312634,0.0001177225,0.0001794966,0.0002706973,0.006218751,0.002707058],"genre_scores_gemma":[0.3451631,0.000208472,0.6480428,0.00009337227,0.00004606003,0.0001582674,0.0007057492,0.0004653553,0.005116752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006723499,"threshold_uncertainty_score":0.01336873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369661373589978,"score_gpt":0.280102261585103,"score_spread":0.2564056478492033,"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."}}