{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005929487,0.0008693931,0.0006164649,0.0009046657,0.0002319614,0.0005792467,0.001162683,0.0009051895,0.001276447],"category_scores_gemma":[0.001076288,0.000390979,0.0005047219,0.0006774689,0.0003374358,0.001164619,0.0007852688,0.0006040725,0.0003413432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008916593,"about_ca_system_score_gemma":0.0008929354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007665889,"about_ca_topic_score_gemma":0.009980384,"domain_scores_codex":[0.9997686,0.00003761864,0.00001133977,0.0000670206,0.00007363777,0.00004185978],"domain_scores_gemma":[0.9997862,0.00006847617,0.00002368792,0.0000292141,0.00006705975,0.00002526826],"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":[0.0005527839,0.0002369087,0.005510984,0.0002403848,0.0002688408,0.0004505677,0.00007872978,0.3065736,0.04023065,0.004991479,0.008120555,0.6327445],"study_design_scores_gemma":[0.00001227903,0.00009777012,0.000459087,0.000007170674,0.00002715177,0.0001282073,0.000009540928,0.9887131,0.008024684,0.0009759047,0.001533985,0.00001104907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1167613,0.00323679,0.8642397,0.0006540595,0.0003029596,0.0002017461,0.0003527266,0.007912252,0.006338476],"genre_scores_gemma":[0.6979209,0.001121712,0.2921945,0.0005553704,0.0001067259,0.0001074787,0.0007490186,0.0002827724,0.006961472],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007665889,"threshold_uncertainty_score":0.01524258,"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."}}