{"id":"W3044795341","doi":"","title":"Medical Image Segmentation by Deep Convolutional Neural Networks","year":2019,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Segmentation; Artificial intelligence; Convolutional neural network; Computer science; Deep learning; Image segmentation; Scale-space segmentation; Task (project management); Pattern recognition (psychology); Segmentation-based object categorization; Computer vision; Engineering","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008699706,0.0003530734,0.0006033898,0.0007975963,0.0005558036,0.000131647,0.0005588252,0.0006526782,0.0006560308],"category_scores_gemma":[0.0003771184,0.0003740212,0.0002990564,0.0007465141,0.0004582727,0.0002395712,0.0001318731,0.003804061,0.0000726056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009659764,"about_ca_system_score_gemma":0.001350783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003775182,"about_ca_topic_score_gemma":0.0005495913,"domain_scores_codex":[0.9951531,0.0005841666,0.0003964013,0.0008398733,0.002140292,0.0008861297],"domain_scores_gemma":[0.9976737,0.0004108026,0.0002207564,0.0004812443,0.0003743699,0.0008390711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01657811,0.002934457,0.4451649,0.005053797,0.005076147,0.03738569,0.002489323,0.001060097,0.1117014,0.007940605,0.2916023,0.07301317],"study_design_scores_gemma":[0.01314448,0.002562003,0.1356155,0.001588218,0.001005599,0.001213558,0.007331843,0.7338632,0.007694955,0.0001821258,0.09365526,0.002143236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8634993,0.001272377,0.002926355,0.003081793,0.003123183,0.001254526,0.0000142664,0.0002137517,0.1246144],"genre_scores_gemma":[0.8922268,0.0005173614,0.00007720166,0.0001259712,0.0009903527,0.000006051647,0.00222078,0.00009573951,0.1037398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7328032,"threshold_uncertainty_score":0.9998712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096096328986024,"score_gpt":0.2988526307904836,"score_spread":0.2878916675006233,"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."}}