{"id":"W3091878842","doi":"10.1016/j.media.2020.101838","title":"ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Jiangsu Provincial Key Research and Development Program; China Scholarship Council; National Natural Science Foundation of China","keywords":"Convolutional neural network; Artificial intelligence; Segmentation; Computer science; Lesion; Pattern recognition (psychology); Esophageal cancer; Deep learning; Image segmentation; Medicine; Pathology; Cancer","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":[0.0002407078,0.0001144521,0.0003261291,0.00009307347,0.0001716829,0.00003354125,0.00005729902,0.00006339117,0.0007486272],"category_scores_gemma":[0.0003415006,0.00009226212,0.0001739267,0.0006530333,0.0001407531,0.00009641958,0.00003461269,0.000137239,0.00001046721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009269742,"about_ca_system_score_gemma":0.0001566437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005293795,"about_ca_topic_score_gemma":0.00001697523,"domain_scores_codex":[0.9984909,0.00007797898,0.0002826556,0.0002920399,0.0006016899,0.0002547071],"domain_scores_gemma":[0.9989654,0.0001672047,0.00007334262,0.0001134136,0.0001120776,0.0005686046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001342907,0.001109827,0.7291805,0.001241106,0.0125446,0.001230546,0.001253512,0.002009724,0.009044047,0.001119011,0.01460473,0.2253195],"study_design_scores_gemma":[0.001522252,0.0002347517,0.1230257,0.00001700986,0.002054515,0.00001226842,0.00009179284,0.8727272,0.0001515148,0.00006053893,0.00003644367,0.00006607405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7393811,0.005013909,0.2301651,0.02390258,0.0000580648,0.001178753,0.00006669396,0.0001321446,0.0001016473],"genre_scores_gemma":[0.9842168,0.00007316449,0.0132255,0.001586869,0.0003643401,0.00006759492,0.0004062936,0.00001210586,0.00004730751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8707174,"threshold_uncertainty_score":0.8196942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06490156277561017,"score_gpt":0.3793603688123824,"score_spread":0.3144588060367723,"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."}}