{"id":"W7055017830","doi":"","title":"Analyzing Alzheimer's disease progression from sequential magnetic resonance imaging scans using deep convolutional neural networks","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Laser Design and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada First Research Excellence Fund","keywords":"Magnetic resonance imaging; Disease; Functional magnetic resonance imaging; Convolutional neural network; Neural activity","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002426375,0.0008665835,0.0006227646,0.0002928214,0.0009126755,0.000210541,0.0006530211,0.0004339304,0.0002788146],"category_scores_gemma":[0.00005320198,0.0009914777,0.0003711886,0.0006000475,0.00007730058,0.0007864067,0.0001056405,0.001368101,0.0001029845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005697175,"about_ca_system_score_gemma":0.00006734631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002675025,"about_ca_topic_score_gemma":0.0001793204,"domain_scores_codex":[0.9962955,0.0001938084,0.0008523033,0.00110652,0.0006507942,0.000901034],"domain_scores_gemma":[0.9979857,0.0001489769,0.0002900455,0.0008181481,0.0002412397,0.0005158411],"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":[0.000607667,0.0003164723,0.003597035,0.0004388511,0.0005725343,0.000290911,0.00002262516,0.385442,0.0503151,0.008967252,0.00005548388,0.5493741],"study_design_scores_gemma":[0.0008938162,0.00002291618,0.00865898,0.000797226,0.001068039,0.00001135643,0.0000523231,0.9789964,0.002534591,0.001536158,0.003901925,0.001526274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279124,0.05551688,0.0003070862,0.00001877567,0.004201666,0.002190543,0.00428127,0.001408835,0.004162594],"genre_scores_gemma":[0.9918305,0.0002548038,0.001528134,0.00006394213,0.0002419155,0.0002083272,0.005253728,0.0003333422,0.0002853413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5935544,"threshold_uncertainty_score":0.9992536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157190111396181,"score_gpt":0.2459134754329757,"score_spread":0.2301944642933576,"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."}}