{"id":"W4402186871","doi":"10.32920/26871346.v1","title":"Unsupervised Domain Adaptation With Boundary-Aware GAN for Medical Image Segmentation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Domain adaptation; Adaptation (eye); Boundary (topology); Domain (mathematical analysis); Segmentation; Image (mathematics); Computer science; Artificial intelligence; Computer vision; Image segmentation; Pattern recognition (psychology); Mathematics; Psychology; Neuroscience; Mathematical analysis","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":[0.0007549982,0.0007030168,0.0006994162,0.0005348468,0.0001734765,0.0005202221,0.001081375,0.0008742308,0.001400074],"category_scores_gemma":[0.001785532,0.0004070169,0.0007207491,0.0005559049,0.0005011564,0.0007196784,0.0007713042,0.001336195,0.0007914271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005414736,"about_ca_system_score_gemma":0.000489725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00226546,"about_ca_topic_score_gemma":0.00409341,"domain_scores_codex":[0.9996958,0.00009904808,0.000009906259,0.0000969822,0.00006655687,0.00003185671],"domain_scores_gemma":[0.9995458,0.0002040826,0.00003876282,0.000117717,0.00006984317,0.00002389477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002989576,0.0001249842,0.001114677,0.0001343541,0.0001340006,0.0001495306,0.0000938306,0.5321028,0.04505259,0.00699692,0.0102181,0.4035793],"study_design_scores_gemma":[0.00000640903,0.00002081715,0.0002230539,0.000005807457,0.000006668959,0.00006593216,0.000005456168,0.989862,0.004906115,0.003805242,0.001084713,0.000007729232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01477806,0.0004949742,0.9804581,0.000196828,0.00005568545,0.00004321758,0.0001325431,0.002189141,0.001651442],"genre_scores_gemma":[0.4435493,0.0006221703,0.5455614,0.0008316603,0.0001320483,0.0001835901,0.001556223,0.0007112313,0.006852325],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00226546,"threshold_uncertainty_score":0.004683733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885772301747691,"score_gpt":0.2953455631136179,"score_spread":0.276487840096141,"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."}}