{"id":"W3042519501","doi":"10.1016/j.media.2020.101791","title":"Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":126,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Hyperintensity; Stroke (engine); Segmentation; Convolutional neural network; Lesion; Magnetic resonance imaging; Artificial intelligence; Ischemic stroke; Medicine; Computer science; Pattern recognition (psychology); Cardiology; Radiology; Ischemia; Pathology","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":[0.0007332293,0.0006693926,0.0005828592,0.001160545,0.0003044449,0.0007212043,0.0006490382,0.0008685237,0.001414192],"category_scores_gemma":[0.001377298,0.0003278643,0.0005666418,0.000560811,0.0002142586,0.0004547605,0.0005980526,0.0006979124,0.0004168655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008268362,"about_ca_system_score_gemma":0.0009899263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02262348,"about_ca_topic_score_gemma":0.02458323,"domain_scores_codex":[0.9998279,0.00003254148,0.00001009823,0.00005213645,0.00003419678,0.00004308123],"domain_scores_gemma":[0.9996969,0.0001380475,0.00003049784,0.00002683247,0.00008607926,0.00002170901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009076745,0.0002991395,0.0125035,0.0002293348,0.0002813917,0.0003515506,0.0001151439,0.1451983,0.05099385,0.003608716,0.01009542,0.775416],"study_design_scores_gemma":[0.000008246391,0.00003830547,0.004381648,0.00001660943,0.0000627288,0.00008918784,0.00001092407,0.9837582,0.009055753,0.001582186,0.0009863399,0.000009877472],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2397775,0.005746276,0.7434969,0.001145902,0.0002643764,0.0001462353,0.001322539,0.003381876,0.004718381],"genre_scores_gemma":[0.8907288,0.00143729,0.1009553,0.0002977511,0.0001349741,0.00007035957,0.0009846374,0.0001413573,0.005249471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02262348,"threshold_uncertainty_score":0.04498357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0293344987829249,"score_gpt":0.3163412260699489,"score_spread":0.287006727287024,"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."}}