{"id":"W4404693793","doi":"10.3390/s24237473","title":"MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shanghai","keywords":"Artificial intelligence; Segmentation; Computer science; Convolutional neural network; Pattern recognition (psychology); Inference; Image segmentation; Transformer; Artificial neural network; Deep learning; Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001464097,0.00011332,0.0001532831,0.0000697431,0.00006287486,0.00003153048,0.00001616413,0.00006660495,0.00001121082],"category_scores_gemma":[0.000004522348,0.00009791575,0.00002984828,0.0004966978,0.00005451215,0.00008609694,0.000004339796,0.000174056,0.000007932545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001113599,"about_ca_system_score_gemma":0.00003933182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002736473,"about_ca_topic_score_gemma":0.0009832501,"domain_scores_codex":[0.9992248,0.00002440494,0.0001617328,0.0002466532,0.0001248191,0.0002175973],"domain_scores_gemma":[0.999751,0.00004782292,0.00003062802,0.00008157714,0.0000291964,0.00005974569],"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":[0.09147619,0.0006460458,0.1250671,0.001858737,0.0008877363,0.0002928782,0.00970479,0.2126955,0.2242142,0.002326909,0.003696003,0.3271339],"study_design_scores_gemma":[0.004554216,0.003873331,0.1835041,0.0003722382,0.0002204782,0.0003032238,0.0003187382,0.8029137,0.002108157,0.0001626894,0.001357322,0.0003117628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886071,0.0007942028,0.008476972,0.0008275644,0.0001585942,0.0006803969,0.000003494969,0.0001067959,0.000344879],"genre_scores_gemma":[0.9981946,0.00005372845,0.001146057,0.0001066716,0.0002398924,0.0000650064,0.00004351605,0.00002038874,0.0001301616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5902182,"threshold_uncertainty_score":0.3992888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00857722584169689,"score_gpt":0.2489765779184528,"score_spread":0.2403993520767559,"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."}}