{"id":"W3214565892","doi":"10.18280/ria.350505","title":"SRGAN Assisted Encoder-Decoder Deep Neural Network for Colorectal Polyp Semantic Segmentation","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Segmentation; Encoder; Benchmark (surveying); Colonoscopy; Computer science; Colorectal Polyp; Deep learning; Sørensen–Dice coefficient; Dice; Colorectal cancer; Artificial neural network; Pattern recognition (psychology); Image segmentation; Radiology; Medicine; Cancer; Internal medicine; Mathematics; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002401964,0.0001828691,0.0003085539,0.00007214911,0.000260964,0.00006436343,0.00007824595,0.0001216861,0.0003293487],"category_scores_gemma":[0.0002225431,0.0001889433,0.0002120149,0.0006068385,0.00005650721,0.0000962611,0.00004039353,0.0001985793,0.00007189751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297696,"about_ca_system_score_gemma":0.00009135954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004579082,"about_ca_topic_score_gemma":0.0003603415,"domain_scores_codex":[0.9984394,0.00006261111,0.000434819,0.0004696017,0.0001878825,0.0004057079],"domain_scores_gemma":[0.9989418,0.0002263843,0.000120267,0.0002845507,0.000283794,0.0001432377],"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":[0.005987142,0.0007930829,0.01112336,0.0006911342,0.0003355578,0.0002369822,0.003237331,0.1513583,0.2053939,0.0006923774,0.003071342,0.6170795],"study_design_scores_gemma":[0.000256243,0.00123227,0.002507411,0.000112205,0.0001378786,0.0004590114,0.001188898,0.6931925,0.2990809,0.0003437921,0.001255398,0.0002333711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5688906,0.001100754,0.4249502,0.001185218,0.001183579,0.0007119046,0.000008516768,0.0001894194,0.001779861],"genre_scores_gemma":[0.9885967,0.00004196701,0.008135828,0.0003614908,0.0005479792,0.0001147013,0.00010533,0.0000352749,0.002060697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6168461,"threshold_uncertainty_score":0.7704885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04396579066490906,"score_gpt":0.3057317565073456,"score_spread":0.2617659658424365,"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."}}