{"id":"W3137531678","doi":"10.1109/tmi.2021.3067688","title":"Constrained Domain Adaptation for Image Segmentation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Image segmentation; Computer vision; Artificial intelligence; Computer science; Image (mathematics); Adaptation (eye); Scale-space segmentation; Domain adaptation; Segmentation; Domain (mathematical analysis); Pattern recognition (psychology); Mathematics; Physics; Optics","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":[0.0009575475,0.001189609,0.0009177427,0.0008112526,0.0003829282,0.000856682,0.001616603,0.001576087,0.00382905],"category_scores_gemma":[0.00276632,0.000639309,0.001093653,0.001116437,0.001246849,0.001518288,0.001996687,0.002548979,0.001524275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159086,"about_ca_system_score_gemma":0.001002033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004139871,"about_ca_topic_score_gemma":0.004367098,"domain_scores_codex":[0.999521,0.0001284421,0.00001830151,0.0001678288,0.0001124724,0.00005185268],"domain_scores_gemma":[0.9992948,0.00033921,0.00007302425,0.0001664702,0.00008802661,0.00003851213],"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":[0.00007563668,0.0000497657,0.0003515174,0.0001200851,0.00006616794,0.00009425677,0.00008200471,0.8436322,0.01174664,0.015625,0.005301757,0.122855],"study_design_scores_gemma":[0.000004351664,0.00001082764,0.00009101946,0.000009072581,0.00000510067,0.00003375273,0.000007183425,0.9823306,0.002318343,0.0132091,0.001972959,0.000007628375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00547181,0.0003869773,0.9909932,0.0001657834,0.00004892968,0.00003401584,0.0001106147,0.00115936,0.001629355],"genre_scores_gemma":[0.4011043,0.001533227,0.5815427,0.0008932204,0.0002126412,0.0003919885,0.001616036,0.00140147,0.01130434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004139871,"threshold_uncertainty_score":0.01280951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01822661270530067,"score_gpt":0.2846312113572855,"score_spread":0.2664045986519848,"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."}}