{"id":"W2901038558","doi":"10.1016/j.neuroimage.2019.03.026","title":"Unsupervised domain adaptation for medical imaging segmentation with self-ensembling","year":2019,"lang":"en","type":"preprint","venue":"NeuroImage","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canada First Research Excellence Fund; Canadian Institutes of Health Research; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Foundation for Innovation; Réseau en Bio-Imagerie du Quebec","keywords":"Computer science; Segmentation; Generalization; Artificial intelligence; Domain adaptation; Domain (mathematical analysis); Task (project management); Adaptation (eye); Medical imaging; Machine learning; Modality (human–computer interaction); Deep learning; Image (mathematics); Image segmentation; Pattern recognition (psychology); Psychology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001906129,0.0008248247,0.001496859,0.0008028617,0.0005290761,0.0008156839,0.001499979,0.001822377,0.001228359],"category_scores_gemma":[0.004006983,0.0007495314,0.00104703,0.0005660253,0.000894101,0.001254944,0.001539201,0.001689986,0.0006246796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006251312,"about_ca_system_score_gemma":0.0009184373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003467208,"about_ca_topic_score_gemma":0.005062769,"domain_scores_codex":[0.9995794,0.0001709688,0.00002146746,0.0001276949,0.00006201499,0.00003856478],"domain_scores_gemma":[0.9988126,0.0006943004,0.00006321854,0.0001864152,0.0001886893,0.00005477604],"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":[0.0003157301,0.0001792369,0.001230178,0.0002515782,0.0002310175,0.0001193284,0.0001970502,0.6717636,0.02186775,0.0123464,0.005500139,0.285998],"study_design_scores_gemma":[0.000004291706,0.00001352931,0.0001277342,0.000006483664,0.000006844889,0.00002504692,0.000004972886,0.9934427,0.001521816,0.004575393,0.0002658595,0.000005340371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009286393,0.0004658559,0.9890455,0.00009155491,0.00003149743,0.00002403928,0.00003588451,0.0007374718,0.0002818769],"genre_scores_gemma":[0.3717199,0.0005249725,0.6224129,0.0003004295,0.00009751074,0.0001866714,0.0005746644,0.0005513964,0.003631627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003467208,"threshold_uncertainty_score":0.0100807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208971409744873,"score_gpt":0.2715188129530055,"score_spread":0.2494290988555568,"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."}}