{"id":"W4366204133","doi":"10.1101/2023.04.17.537196","title":"mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"National Institutes of Health","keywords":"Computer science; MATLAB; Segmentation; Commodity; Deep learning; Artificial intelligence; Human–computer interaction; Business; Operating system","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":[0.001056398,0.002042029,0.0008788259,0.00145592,0.0003601618,0.001603062,0.003163587,0.0009506942,0.08912171],"category_scores_gemma":[0.005498101,0.0009991113,0.001170344,0.0008598033,0.0005906769,0.001949616,0.003257398,0.001800091,0.03569784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008884828,"about_ca_system_score_gemma":0.001106551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001487996,"about_ca_topic_score_gemma":0.002608337,"domain_scores_codex":[0.999443,0.00007000943,0.00005027211,0.0001582843,0.0001998048,0.00007865917],"domain_scores_gemma":[0.9985644,0.0007681453,0.0001061197,0.0001987009,0.0002529756,0.0001097404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001620511,0.0001610296,0.003685714,0.002162136,0.0003501337,0.0014114,0.0009192581,0.0240199,0.03065928,0.02100255,0.6182185,0.2957897],"study_design_scores_gemma":[0.0007658001,0.0002182744,0.005182572,0.0006337339,0.0001159312,0.001636999,0.0002824374,0.3045513,0.1166478,0.0626202,0.5069754,0.000369613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.003301096,0.0002432463,0.4162716,0.000242485,0.0001135561,0.000111819,0.01152098,0.5630771,0.005118095],"genre_scores_gemma":[0.1229148,0.001041108,0.5267391,0.001687362,0.0001329757,0.002585075,0.05202138,0.2624282,0.03044996],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.08912171,"threshold_uncertainty_score":0.2981418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349117459481966,"score_gpt":0.2916420342239535,"score_spread":0.2567302882757568,"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."}}