{"id":"W4368282560","doi":"10.47611/jsrhs.v12i1.3910","title":"Finding the Most Effective Data Augmentation Techniques on Brain MRI Data Using Deep Networks","year":2023,"lang":"en","type":"article","venue":"Journal of Student Research","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Magnetic resonance imaging; Medicine; Cognition; Data set; Computer science; Noise (video); Set (abstract data type); Glioma; Artificial intelligence; Radiology; Neuroscience; Psychology; Image (mathematics)","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":[0.008224674,0.00008652554,0.0001184159,0.0004028133,0.0006786421,0.0003366929,0.002214745,0.00004674706,0.00002262779],"category_scores_gemma":[0.002637109,0.00005832394,0.0000263039,0.001615089,0.0001466591,0.0005426012,0.001072368,0.0009011163,0.00002557298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002145233,"about_ca_system_score_gemma":0.00007340622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001338975,"about_ca_topic_score_gemma":0.00001658623,"domain_scores_codex":[0.9962993,0.001205891,0.0003315032,0.0003562103,0.001507563,0.0002995175],"domain_scores_gemma":[0.9951628,0.003462491,0.0002375905,0.0009119295,0.0001551424,0.0000700218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005370393,0.00055281,0.004019948,0.00004654163,0.00009616167,0.0003024875,0.002081028,0.005308243,0.6087921,0.001316745,0.1212139,0.255733],"study_design_scores_gemma":[0.001791338,0.00162565,0.1296265,0.0005317608,0.00006192202,0.0003959404,0.009091815,0.6781008,0.147117,0.0008332678,0.03039594,0.0004280291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9372468,0.0001168754,0.03026002,0.02684507,0.001396072,0.002306091,0.0000505293,0.00015701,0.001621537],"genre_scores_gemma":[0.9985083,0.0002764798,0.000113558,0.000417734,0.0004872731,0.00001230702,0.00001543423,0.00001774434,0.0001511305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6727926,"threshold_uncertainty_score":0.5219635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4747131322959252,"score_gpt":0.5394934077107888,"score_spread":0.06478027541486359,"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."}}