{"id":"W3017135785","doi":"10.1109/tnnls.2020.2984955","title":"Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Medical Research Council; Special Project for Research and Development in Key areas of Guangdong Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Deep learning; Multi-task learning; Magnetic resonance imaging; Adversarial system; Pattern recognition (psychology); Modality (human–computer interaction); Machine learning; Task (project management); Radiology; Medicine; Engineering","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":[0.002172371,0.001196977,0.0008345681,0.0005543669,0.0002716276,0.0005382526,0.001085826,0.0009865068,0.000801459],"category_scores_gemma":[0.004842426,0.0003822861,0.0008840801,0.0004184326,0.000657622,0.0009713707,0.00129895,0.001747002,0.0003227013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005072,"about_ca_system_score_gemma":0.0005856971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002799497,"about_ca_topic_score_gemma":0.002593296,"domain_scores_codex":[0.99942,0.000218994,0.00002638167,0.0001677315,0.000102335,0.00006456248],"domain_scores_gemma":[0.9985214,0.0008715119,0.0001655429,0.0001730522,0.0001957562,0.00007266662],"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":[0.0002552725,0.0001442961,0.003529369,0.00007896938,0.000104039,0.0002437737,0.0001091046,0.8316748,0.01089974,0.004444526,0.001984016,0.146532],"study_design_scores_gemma":[0.000002863262,0.00002434474,0.0002989075,0.000003323553,0.000006301746,0.00004032212,0.000004690488,0.9965233,0.001242249,0.001689609,0.0001581226,0.000006089088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04785336,0.0003874505,0.949886,0.0002515364,0.00005638535,0.00005221779,0.00008824978,0.0005400673,0.0008847229],"genre_scores_gemma":[0.8409638,0.0002963422,0.1550363,0.0002828072,0.00009931972,0.0001147757,0.0004268762,0.0001253607,0.002654481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002799497,"threshold_uncertainty_score":0.01148868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02348687710062469,"score_gpt":0.2770911227830383,"score_spread":0.2536042456824136,"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."}}