{"id":"W4401798517","doi":"10.1088/2057-1976/ad72f9","title":"An hetero-modal deep learning framework for medical image synthesis applied to contrast and non-contrast MRI","year":2024,"lang":"en","type":"article","venue":"Biomedical Physics & Engineering Express","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Contrast (vision); Modality (human–computer interaction); Artificial intelligence; Modalities; Representation (politics); Generator (circuit theory); Deep learning; Pattern recognition (psychology); Machine learning","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":[0.0006583328,0.0004432947,0.0002936306,0.0003194499,0.0001818193,0.0004906195,0.000712144,0.000689174,0.002046125],"category_scores_gemma":[0.0008515851,0.0002541259,0.0005617359,0.000264897,0.0004867857,0.0005974518,0.0008151209,0.001064322,0.0004028597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007123164,"about_ca_system_score_gemma":0.0006153792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003192955,"about_ca_topic_score_gemma":0.004942744,"domain_scores_codex":[0.9998552,0.00004299666,0.000005460583,0.00003825603,0.00003985995,0.00001821594],"domain_scores_gemma":[0.9998511,0.00006659548,0.00001588057,0.00002280823,0.0000289427,0.00001466091],"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":[0.0001030191,0.0000491301,0.0003146446,0.00005603203,0.00005512721,0.0000844129,0.00005052128,0.8289027,0.01838752,0.02151648,0.001549492,0.128931],"study_design_scores_gemma":[0.000002175229,0.00001146968,0.00004027394,0.000002555695,0.000002879225,0.00001214375,0.000002008925,0.9942009,0.001345408,0.003913321,0.000464686,0.000002130601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006780809,0.0002133122,0.9912847,0.0001551713,0.00002620747,0.0000149554,0.00005056337,0.0004706036,0.001003745],"genre_scores_gemma":[0.585998,0.0004887649,0.4062576,0.0003643438,0.00007584706,0.0001294347,0.0003081836,0.0001981492,0.006179728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003192955,"threshold_uncertainty_score":0.006844997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00562474444758538,"score_gpt":0.2589386605571121,"score_spread":0.2533139161095267,"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."}}