{"id":"W4412831604","doi":"10.1162/imag.a.116","title":"Robust deep MRI contrast synthesis using a prior-based and task-oriented 3D network","year":2025,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Université de Bordeaux; Ministerio de Ciencia e Innovación; Universitat Politècnica de València; Agence Nationale de la Recherche","keywords":"Contrast (vision); Computer science; Task (project management); Artificial intelligence; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005618989,0.0001752208,0.000185546,0.0001966616,0.0004364124,0.0004677654,0.0008219697,0.00002736277,0.000005146416],"category_scores_gemma":[0.0007896219,0.0001705831,0.00003577278,0.001289756,0.0005065416,0.0007399627,0.0003452392,0.0001701309,0.000001671683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005824974,"about_ca_system_score_gemma":0.0001821547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004030113,"about_ca_topic_score_gemma":0.000002063861,"domain_scores_codex":[0.9979368,0.0001559543,0.0002878227,0.0007410857,0.0004009341,0.000477345],"domain_scores_gemma":[0.9988215,0.0003080163,0.0001226844,0.0004980474,0.00009594815,0.0001537959],"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":[0.0000299765,0.0003306688,0.0298046,0.0001699603,0.00001078375,0.0003693622,0.0004362979,0.02291677,0.3044833,0.005359515,0.003707581,0.6323811],"study_design_scores_gemma":[0.0001930914,0.00001330216,0.005319227,0.0001314501,0.00001113734,0.0000199615,0.000008441099,0.973136,0.02034154,0.0001649331,0.0004914107,0.0001694957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002236961,0.0001311426,0.9946226,0.001477815,0.0006174501,0.0002479563,0.000001582406,0.0004263805,0.0002380783],"genre_scores_gemma":[0.3242104,0.00001873635,0.6651209,0.01052637,0.00003578658,0.00003274991,3.588805e-7,0.00001155882,0.00004313026],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9502193,"threshold_uncertainty_score":0.6956175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746864783440241,"score_gpt":0.2733091058618218,"score_spread":0.2558404580274194,"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."}}