{"id":"W2604419855","doi":"10.15353/vsnl.v1i1.56","title":"Multi-Neighborhood Convolutional Networks","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scale (ratio); Feature (linguistics); Image (mathematics); Convolutional neural network; Pattern recognition (psychology); Space (punctuation); Artificial intelligence; Computer science; Mathematics; Algorithm; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003358238,0.0005927167,0.0005885331,0.0004369027,0.0002482277,0.0006693435,0.001431761,0.0009493211,0.002909246],"category_scores_gemma":[0.0008398344,0.0003309264,0.0005848124,0.0005511236,0.0004052177,0.00132726,0.0007764017,0.0007104175,0.000835159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008424761,"about_ca_system_score_gemma":0.0004798042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006546313,"about_ca_topic_score_gemma":0.0121581,"domain_scores_codex":[0.9997688,0.00002959151,0.00001005697,0.0000924894,0.00005642291,0.00004277073],"domain_scores_gemma":[0.9997687,0.00004943576,0.00003368513,0.00007011049,0.0000584646,0.000019521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002827682,0.0001156837,0.001713211,0.0001402044,0.0001303976,0.0002396742,0.00006297719,0.5474373,0.03531756,0.03070639,0.008620226,0.3752337],"study_design_scores_gemma":[0.000003179633,0.00001924664,0.0002420068,0.000003336883,0.000008518356,0.00003227149,0.000002454852,0.9932066,0.002757237,0.002634109,0.001087291,0.000003753827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0547839,0.001594841,0.9336635,0.0003835679,0.0001201177,0.00003734719,0.0002858841,0.002059996,0.00707084],"genre_scores_gemma":[0.8188707,0.0008427676,0.162307,0.000304711,0.00009651947,0.00005557308,0.0006754097,0.0001799849,0.01666728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006546313,"threshold_uncertainty_score":0.0130164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02929442153991434,"score_gpt":0.3017065741466955,"score_spread":0.2724121526067811,"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."}}