{"id":"W2917055433","doi":"10.1109/jtehm.2019.2900628","title":"Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Translational Engineering in Health and Medicine","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Segmentation; Encoder; Artificial intelligence; Computer vision; Pixel; Convolutional neural network; Deep learning; Image segmentation; End-to-end principle; Pattern recognition (psychology); Metric (unit)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008179334,0.0001495968,0.0004234311,0.0002468363,0.00007774904,0.00001573185,0.0001752746,0.00004725316,0.000004530548],"category_scores_gemma":[0.00004452634,0.0001366203,0.00007236853,0.0004893233,0.00001504501,0.0003103966,0.00001035836,0.0001917532,7.112905e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001157742,"about_ca_system_score_gemma":0.0001831811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001536054,"about_ca_topic_score_gemma":0.000004191621,"domain_scores_codex":[0.9983356,0.00004161623,0.0007551369,0.0002037344,0.0003146899,0.0003492386],"domain_scores_gemma":[0.9985766,0.0005943998,0.0002598598,0.00014361,0.0001123137,0.0003131618],"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":[0.00002824414,0.00001818592,0.000919072,0.000208319,0.00003471802,8.449049e-7,0.0008005619,0.9455635,0.006954632,0.0009951141,0.00007111455,0.04440568],"study_design_scores_gemma":[0.001336287,0.0003270332,0.004903754,0.0002990314,0.00001751971,0.00002287742,0.00002873723,0.9907066,0.000131529,0.0006991142,0.001388898,0.000138659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06008962,0.002503154,0.9329958,0.002533401,0.001142398,0.0007015971,0.00000606769,0.00002383284,0.000004126115],"genre_scores_gemma":[0.5108825,0.0002908599,0.4873536,0.0005254939,0.0008725852,0.000032424,0.000008364543,0.00002319058,0.0000109973],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4507929,"threshold_uncertainty_score":0.5571213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02994348097145214,"score_gpt":0.3066570326280813,"score_spread":0.2767135516566291,"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."}}