{"id":"W4391916407","doi":"","title":"Adaptation de domaine par une méthode d'apprentissage profond parcimonieuse. Application à la segmentation d'images biomédicales.","year":2023,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"French Infrastructure for Integrated Structural Biology; Centre National de la Recherche Scientifique; Université de Strasbourg; Institute of Genetics; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001292396,0.001184384,0.001100115,0.001176839,0.0006254161,0.001839394,0.001164959,0.002309428,0.006876424],"category_scores_gemma":[0.00364404,0.0005974766,0.0009778221,0.00124429,0.0006157491,0.001274399,0.001200606,0.001276199,0.00312017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005960924,"about_ca_system_score_gemma":0.0009539438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01500568,"about_ca_topic_score_gemma":0.01191836,"domain_scores_codex":[0.9992879,0.0001354061,0.00004136009,0.0002802807,0.0001864357,0.00006868305],"domain_scores_gemma":[0.9985489,0.0005864916,0.00006278824,0.0002766542,0.000471007,0.00005416273],"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":[0.0003585496,0.0002315724,0.001956673,0.0003191571,0.0001251445,0.0004192759,0.0004227417,0.08235189,0.1089919,0.002550646,0.01041731,0.7918552],"study_design_scores_gemma":[0.0000596035,0.0001300158,0.003642386,0.00008135606,0.00005750102,0.0006544089,0.0001954081,0.8828694,0.07754865,0.00287423,0.03183043,0.00005663256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03530754,0.001095492,0.9536717,0.0004049775,0.0004002777,0.0001461301,0.0003681854,0.005324543,0.003281091],"genre_scores_gemma":[0.2144012,0.0008665161,0.7590397,0.0003414582,0.0001585342,0.0002451413,0.00125496,0.001443268,0.02224909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01500568,"threshold_uncertainty_score":0.02983665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06812015622039959,"score_gpt":0.3655989163978228,"score_spread":0.2974787601774232,"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."}}