{"id":"W2525247990","doi":"10.1016/b978-0-12-804076-8.00003-7","title":"Deep learning of brain images and its application to multiple sclerosis","year":2016,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"AI in cancer detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deep learning; Artificial intelligence; Convolutional neural network; Computer science; Boltzmann machine; Neuroimaging; Feature (linguistics); Focus (optics); Pattern recognition (psychology); Machine learning; Dimensionality reduction; Neuroscience; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003034541,0.0005326828,0.0003328238,0.0006905289,0.0001235588,0.0008122753,0.0004527494,0.0006799761,0.008203941],"category_scores_gemma":[0.0009053059,0.0002682633,0.0004055628,0.001004243,0.0002923116,0.0005533475,0.000601052,0.0009709178,0.00227339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003271835,"about_ca_system_score_gemma":0.0004122006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790924,"about_ca_topic_score_gemma":0.005648528,"domain_scores_codex":[0.9999394,0.000007499844,0.000003483091,0.00001437426,0.00002664881,0.000008468971],"domain_scores_gemma":[0.9998066,0.00009522503,0.00001288565,0.00001880387,0.00005280687,0.00001374346],"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":[0.00004542365,0.00004550065,0.0005841149,0.0003050034,0.00004121338,0.0001221703,0.00005155407,0.04038107,0.01292627,0.01717906,0.03406946,0.8942491],"study_design_scores_gemma":[0.00001335606,0.00008380074,0.004313094,0.0002138172,0.0000497393,0.0007672864,0.00006354247,0.7836562,0.02257438,0.08240135,0.1058177,0.00004572865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02616101,0.04535654,0.87332,0.004129912,0.001116735,0.00006167704,0.001511031,0.003768904,0.04457416],"genre_scores_gemma":[0.2947329,0.04569649,0.5009661,0.0007370254,0.0009566718,0.00009794146,0.002051843,0.0006547343,0.1541062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008203941,"threshold_uncertainty_score":0.02744496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01791464114269168,"score_gpt":0.2290017724181533,"score_spread":0.2110871312754616,"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."}}