{"id":"W6963501043","doi":"10.21227/4gqq-er08","title":"Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks","year":2020,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Convolutional neural network; Segmentation; Pattern recognition (psychology); Lesion; Sørensen–Dice coefficient; Artificial neural network; Similarity (geometry); Stroke (engine)","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":[0.001136941,0.001040534,0.0005600012,0.001581887,0.000351944,0.0009240743,0.001036534,0.0007723479,0.0009717349],"category_scores_gemma":[0.003451118,0.0004845894,0.0008474838,0.0008995493,0.0003138943,0.000552207,0.0006635032,0.0004819014,0.0004894354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00110639,"about_ca_system_score_gemma":0.001110137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0143055,"about_ca_topic_score_gemma":0.02442559,"domain_scores_codex":[0.9994671,0.0000814819,0.00006582307,0.0001849758,0.0001253042,0.00007536814],"domain_scores_gemma":[0.9991708,0.0002160789,0.000160488,0.0001808003,0.0002422906,0.00002964506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001622401,0.0006399586,0.0481491,0.0005340684,0.0007115512,0.001194217,0.0003821776,0.2126558,0.2201674,0.00121633,0.007018741,0.5057083],"study_design_scores_gemma":[0.00002579093,0.0001573155,0.03123238,0.000035357,0.0001020983,0.0004253981,0.0000670004,0.8899422,0.07542067,0.0006854644,0.001865717,0.00004045575],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.8258911,0.0009638774,0.1652825,0.0002181428,0.00009209781,0.0003516743,0.002611309,0.003067894,0.001521369],"genre_scores_gemma":[0.881775,0.0002961087,0.10961,0.0001128992,0.00002405696,0.0002194931,0.006159013,0.0002153785,0.001588055],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.0143055,"threshold_uncertainty_score":0.02844447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01733727751603585,"score_gpt":0.244742462559307,"score_spread":0.2274051850432711,"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."}}