{"id":"W3109211147","doi":"10.1007/978-3-030-72084-1_23","title":"Efficient Embedding Network for 3D Brain Tumor Segmentation","year":2021,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Embedding; Segmentation; Artificial intelligence; Convolutional neural network; Dimension (graph theory); Field (mathematics); Encoder; Deep learning; Encoding (memory); Image (mathematics); Pattern recognition (psychology); Artificial neural network; Transfer of learning; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003987472,0.001383154,0.001276027,0.001252995,0.0004110239,0.0009068529,0.001418904,0.001761704,0.004584458],"category_scores_gemma":[0.001492788,0.0008272227,0.0008923357,0.001196983,0.0004658374,0.001360499,0.001716932,0.001161492,0.001885081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007926502,"about_ca_system_score_gemma":0.0007569545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007229655,"about_ca_topic_score_gemma":0.01064079,"domain_scores_codex":[0.999713,0.00005902678,0.0000146266,0.00008884146,0.00008086406,0.00004353271],"domain_scores_gemma":[0.9994424,0.000236601,0.00005184266,0.0001035721,0.000128829,0.00003677956],"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":[0.0003124829,0.0001315919,0.0008700141,0.0001664676,0.0001045645,0.0001566493,0.00009674178,0.468971,0.02264893,0.007390738,0.007526638,0.4916242],"study_design_scores_gemma":[0.000003581563,0.00001374525,0.0001181738,0.000005190589,0.000005801244,0.00002929054,0.000007404973,0.9949542,0.001769599,0.002581174,0.0005079249,0.000003985866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02507744,0.0004106509,0.9707944,0.0002311136,0.00005426149,0.00005746354,0.0005108567,0.001737246,0.001126602],"genre_scores_gemma":[0.3926612,0.0007000562,0.5914518,0.0001939512,0.0001059857,0.0002763593,0.002765076,0.0006513096,0.01119422],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007229655,"threshold_uncertainty_score":0.01533657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0309112218476635,"score_gpt":0.3038966644239267,"score_spread":0.2729854425762632,"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."}}