{"id":"W4286640356","doi":"10.1016/j.neuroimage.2022.119505","title":"DAFT: A universal module to interweave tabular data and 3D images in CNNs","year":2022,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; National Institutes of Health; Bayerisches Staatsministerium für Wissenschaft und Kunst; Takeda Pharmaceutical Company; IXICO; Eisai; Northern California Institute for Research and Education; Bundesministerium für Forschung und Technologie; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Johnson and Johnson; Medpace; University of Southern California; Janssen Research and Development; Nvidia; Leibniz-Rechenzentrum; F. Hoffmann-La Roche; Merck; Alzheimer's Drug Discovery Foundation; National Institute on Aging; Alzheimer's Association","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001236895,0.0001047695,0.000108245,0.0001266966,0.000156159,0.00006836792,0.001735992,0.0000107601,0.00001832137],"category_scores_gemma":[0.00003350052,0.0001219467,0.00001154113,0.0006803353,0.00003886834,0.0005545989,0.005083057,0.0002528337,0.00001800854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003880712,"about_ca_system_score_gemma":0.00002144509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002193499,"about_ca_topic_score_gemma":0.00002172701,"domain_scores_codex":[0.9986817,0.00007966111,0.000134276,0.0006930304,0.00017715,0.0002342281],"domain_scores_gemma":[0.9984524,0.00008089289,0.00004037736,0.001326693,0.00001362827,0.00008603983],"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":[0.0001876061,0.001279197,0.01088662,0.0000678405,0.00003893915,0.004219505,0.006261993,0.0701208,0.2619224,0.05509206,0.1287279,0.4611952],"study_design_scores_gemma":[0.0009290239,0.0003252405,0.01607621,0.00001207444,0.000009484833,0.0001503807,0.0001775812,0.7353213,0.001918297,0.003383508,0.2410928,0.0006041004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1651774,0.0001535266,0.8206192,0.0106792,0.0003020033,0.0007715431,0.0002126139,0.0003315193,0.001753033],"genre_scores_gemma":[0.9372385,0.000020758,0.06039131,0.001817475,0.0000355954,0.00005032467,0.00002162949,0.00001973129,0.0004046689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7720611,"threshold_uncertainty_score":0.6335667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675336740178777,"score_gpt":0.2738923052506021,"score_spread":0.2471389378488143,"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."}}