{"id":"W4388484077","doi":"10.2139/ssrn.4627340","title":"Redundant Co-Training: Semi-Supervised Segmentation of the Left Ventricle Using Informative Redundancy","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Segmentation; Redundancy (engineering); Artificial intelligence; Computer science; Training (meteorology); Ventricle; Pattern recognition (psychology); Machine learning; Medicine; Internal medicine; Geography","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":[0.002260348,0.00137286,0.001793886,0.001622532,0.0006987366,0.001211366,0.002581304,0.002434531,0.001787603],"category_scores_gemma":[0.003951717,0.0009732539,0.001510835,0.001609735,0.0008354433,0.001163747,0.002059013,0.001552,0.001555878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004092279,"about_ca_system_score_gemma":0.001460476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003667505,"about_ca_topic_score_gemma":0.006258388,"domain_scores_codex":[0.9988451,0.0003476139,0.00005900591,0.0003680899,0.0002207407,0.0001593363],"domain_scores_gemma":[0.997798,0.0009360628,0.0002617072,0.0005042373,0.0003890757,0.000111019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00114289,0.0003377456,0.001621805,0.0004364297,0.0003444043,0.000317012,0.0004389538,0.1657332,0.08224073,0.004157596,0.01163729,0.7315919],"study_design_scores_gemma":[0.00001857505,0.00006200097,0.0006234285,0.00001839163,0.00004282743,0.0001320416,0.00002458899,0.9825781,0.01294976,0.002555055,0.000979066,0.00001616146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03048344,0.00048183,0.9647555,0.0001455394,0.00004788171,0.00008059333,0.0003116556,0.002721594,0.0009719642],"genre_scores_gemma":[0.334226,0.000336293,0.6589045,0.0001842031,0.0001383558,0.0002593984,0.002068816,0.0009118033,0.002970605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003667505,"threshold_uncertainty_score":0.01195395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04829234511147931,"score_gpt":0.3166691421960085,"score_spread":0.2683767970845292,"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."}}