{"id":"W3178316354","doi":"10.1007/978-3-030-64340-9_7","title":"AECNN: Adversarial and Enhanced Convolutional Neural Networks","year":2021,"lang":"en","type":"book-chapter","venue":"","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Upsampling; Dilation (metric space); Discriminator; Artificial intelligence; Convolutional neural network; Computer science; Pattern recognition (psychology); Convolution (computer science); Segmentation; Adversarial system; Feature (linguistics); Pixel; Generative adversarial network; Image (mathematics); Deep learning; Artificial neural network; Mathematics","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":[0.0003483264,0.0009212632,0.000345447,0.0005692697,0.0001513239,0.0008123415,0.001082117,0.0008747044,0.02305762],"category_scores_gemma":[0.001201767,0.0003825996,0.0004088914,0.0008570979,0.0002928592,0.001241042,0.0008380169,0.001794136,0.01519082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005314157,"about_ca_system_score_gemma":0.0004190819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002943678,"about_ca_topic_score_gemma":0.006305861,"domain_scores_codex":[0.9998317,0.0000198758,0.000007131364,0.00003881856,0.00009028125,0.00001224794],"domain_scores_gemma":[0.9997111,0.0001368909,0.0000126748,0.0000429445,0.0000831884,0.00001325789],"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":[0.000036533,0.00003476727,0.0001508839,0.0002426789,0.0000304449,0.00006154925,0.00002005864,0.07565702,0.003068901,0.05112807,0.1610671,0.7085021],"study_design_scores_gemma":[0.000009430601,0.00003416112,0.0004667197,0.0001760367,0.00002340516,0.0002996025,0.00001274499,0.4452437,0.008696856,0.09644629,0.4485516,0.00003948899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00154587,0.01261566,0.9057544,0.0009811838,0.001456347,0.00005517488,0.001601675,0.005708926,0.07028075],"genre_scores_gemma":[0.04310919,0.02308396,0.5505459,0.001087071,0.001217286,0.0002144004,0.006011747,0.003076562,0.371654],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02305762,"threshold_uncertainty_score":0.07713538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615377538640441,"score_gpt":0.2384184758536745,"score_spread":0.22226470046727,"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."}}