{"id":"W2950994280","doi":"10.1016/j.media.2019.101557","title":"Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation","year":2019,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dropout (neural networks); Segmentation; Voxel; Artificial intelligence; Computer science; Context (archaeology); Sigmoid function; Image segmentation; Pattern recognition (psychology); Deep learning; Machine learning; Lesion; Monte Carlo method; Artificial neural network; Medicine; Mathematics; Statistics; Pathology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001079744,0.000293569,0.000571752,0.0004453177,0.00007414066,0.00009561887,0.0002549466,0.0004679844,0.00002194616],"category_scores_gemma":[0.0007982393,0.000288038,0.0004763102,0.0004227376,0.00009584609,0.00001455838,0.0004671844,0.0003598279,0.00000113201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008125501,"about_ca_system_score_gemma":0.00004668042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008827383,"about_ca_topic_score_gemma":0.007078245,"domain_scores_codex":[0.9977022,0.0001974525,0.0005244479,0.000884277,0.0004011703,0.0002903946],"domain_scores_gemma":[0.9988058,0.00008849319,0.0002304544,0.0005583857,0.0001824484,0.0001343809],"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":[0.0003247221,0.0002205558,0.0307875,0.0002228675,0.002822803,0.00001078905,0.0001751651,0.0649519,0.5281003,6.156282e-7,0.0002284825,0.3721544],"study_design_scores_gemma":[0.000896825,0.0001064966,0.01227793,0.0001206324,0.002161112,0.000001285621,0.0002122202,0.8067649,0.1765746,0.00003156157,0.0003260308,0.000526356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3873441,0.0007824569,0.6113824,0.00004913679,0.000037482,0.0003523241,0.000003988786,0.00002398623,0.00002412991],"genre_scores_gemma":[0.9879213,0.006645195,0.003605317,0.0001101439,0.0001686325,0.0004841552,0.0009966879,0.00003367307,0.00003489944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.741813,"threshold_uncertainty_score":0.9999572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05985485075319712,"score_gpt":0.2952928520112744,"score_spread":0.2354380012580772,"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."}}