{"id":"W3211288915","doi":"10.3390/s21217018","title":"Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; St. Michael's Hospital","funders":"Ryerson University","keywords":"Generalizability theory; Computer science; Medical imaging; Artificial intelligence; Segmentation; CMA-ES; Machine learning; Set (abstract data type); Deep learning; Adaptation (eye); Selection (genetic algorithm); Variety (cybernetics); Data mining; Evolution strategy; Evolutionary algorithm; Mathematics","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":[0.0006161485,0.0001365959,0.0001746982,0.00006576041,0.0001637373,0.00007276441,0.0009256874,0.0001490712,0.0001057838],"category_scores_gemma":[0.0006859896,0.0001457194,0.00003790288,0.0006471897,0.00006266117,0.0004221672,0.0005264109,0.0003788814,0.00001993057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007397663,"about_ca_system_score_gemma":0.0001437286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001356507,"about_ca_topic_score_gemma":0.0002501455,"domain_scores_codex":[0.9976563,0.0001767568,0.0004036468,0.0006875902,0.0007020615,0.0003736234],"domain_scores_gemma":[0.9982697,0.000691187,0.0001148576,0.0006266638,0.00007865566,0.0002188972],"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":[0.00003011476,0.0001349318,0.0009083776,0.00001912296,0.00002638812,0.0001663725,0.000510584,0.4995311,0.0001082683,0.01069974,0.001327869,0.4865371],"study_design_scores_gemma":[0.0007815199,0.0000266898,0.0009412243,0.00003338823,0.000006733568,0.00005647791,0.0001415229,0.9926339,0.0002176543,0.0004450108,0.004567026,0.0001487989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04733469,0.0001968031,0.9459333,0.005634567,0.0003272444,0.0002801545,0.000002943833,0.0001387479,0.0001515732],"genre_scores_gemma":[0.7504121,0.0007203375,0.2436852,0.002306839,0.0008001056,0.0001923306,0.001329178,0.00005143812,0.0005024892],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7030774,"threshold_uncertainty_score":0.5942265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03043058301822709,"score_gpt":0.3365272159018703,"score_spread":0.3060966328836432,"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."}}