{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006543573,0.001171296,0.0008270883,0.001452893,0.0003168964,0.001075734,0.001304973,0.001358451,0.00255198],"category_scores_gemma":[0.001508395,0.0007552444,0.001206946,0.00111695,0.0005468532,0.001105851,0.001037846,0.001184482,0.001311881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001570575,"about_ca_system_score_gemma":0.001172115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01289928,"about_ca_topic_score_gemma":0.01831763,"domain_scores_codex":[0.9995503,0.00006491852,0.00002805007,0.0001482283,0.000140773,0.0000677938],"domain_scores_gemma":[0.9996617,0.0001111081,0.0000577193,0.0000575466,0.00009004623,0.00002187057],"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":[0.0002711476,0.0000964617,0.001359194,0.0001990388,0.0001518699,0.000142244,0.00006833378,0.4218543,0.03515247,0.005561564,0.006711902,0.5284315],"study_design_scores_gemma":[0.000004209935,0.00001602678,0.000246758,0.000009137709,0.00001029267,0.00003000465,0.000004202342,0.9917383,0.004714542,0.002118066,0.001101791,0.00000670958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02003319,0.001603822,0.9697963,0.0004177936,0.0000838419,0.00009710029,0.0003779283,0.005442365,0.002147716],"genre_scores_gemma":[0.3824612,0.002125256,0.6028519,0.0006906732,0.0001417841,0.0001848215,0.002285524,0.0004847204,0.008774194],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01289928,"threshold_uncertainty_score":0.02564842,"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."}}