{"id":"W4295469822","doi":"10.1109/embc48229.2022.9871402","title":"Edge-preserving Image Synthesis for Unsupervised Domain Adaptation in Medical Image Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Artificial intelligence; Domain (mathematical analysis); Adaptation (eye); Domain adaptation; Relevance (law); Pattern recognition (psychology); Enhanced Data Rates for GSM Evolution; Image (mathematics); Image segmentation; Labeled data; Process (computing); Transformation (genetics); Computer vision; Mathematics","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":[0.002111337,0.0001947129,0.0003118431,0.0002994802,0.0001340956,0.00002904902,0.001649796,0.0001060639,0.0005310222],"category_scores_gemma":[0.001225076,0.0001723449,0.0001432927,0.000706577,0.0001633959,0.0003297834,0.0004002041,0.000572613,0.000002841564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002131259,"about_ca_system_score_gemma":0.0001806312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002399619,"about_ca_topic_score_gemma":0.0001123615,"domain_scores_codex":[0.9978094,0.0002693509,0.0006072152,0.0004075489,0.0005937303,0.0003127389],"domain_scores_gemma":[0.9983067,0.0008366472,0.0002296324,0.0003066017,0.0002423555,0.00007806934],"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":[0.0005091499,0.000950023,0.01326077,0.0005973559,0.0007575357,0.00002821945,0.1282551,0.1916382,0.5207114,0.0846431,0.0359318,0.0227174],"study_design_scores_gemma":[0.002115267,0.0001279657,0.004217136,0.0001801202,0.00001327983,0.00001743189,0.007046888,0.9745134,0.0006191409,0.002353695,0.008496068,0.0002996796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1223297,0.0001134087,0.8629494,0.01138884,0.002083943,0.0004826697,0.0000700262,0.00007918122,0.0005029203],"genre_scores_gemma":[0.8834535,0.0001695542,0.1140821,0.0009546501,0.0002465931,0.0004960303,0.0001611846,0.00002981728,0.0004065949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7828751,"threshold_uncertainty_score":0.7028022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03940838813691636,"score_gpt":0.3070782386497385,"score_spread":0.2676698505128222,"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."}}