{"id":"W4413774835","doi":"10.1016/j.compbiomed.2025.110986","title":"Polyp segmentation in colonoscopy images using DeepLabV3++","year":2025,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"","keywords":"Colonoscopy; Artificial intelligence; Computer science; Computer vision; Segmentation; Medicine; Internal medicine; Colorectal 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003154568,0.001749668,0.0007574391,0.001715121,0.0004573035,0.001321014,0.001545056,0.001259372,0.01241671],"category_scores_gemma":[0.0009929177,0.0007475594,0.001046475,0.001106199,0.0002040336,0.0005558581,0.001004403,0.0008232347,0.003943221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005979648,"about_ca_system_score_gemma":0.001239451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01630756,"about_ca_topic_score_gemma":0.02448565,"domain_scores_codex":[0.9998237,0.00001460618,0.00001254435,0.00005668214,0.00004895845,0.00004354097],"domain_scores_gemma":[0.9998386,0.00004749171,0.0000161802,0.00002424733,0.00004862517,0.00002477891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001381395,0.0003252151,0.003997783,0.001146264,0.0003537874,0.0004727526,0.0001946918,0.05324589,0.0505425,0.001926215,0.07342681,0.8129867],"study_design_scores_gemma":[0.0001512204,0.0002821283,0.003409503,0.0001065788,0.0001127371,0.0006071141,0.00009537122,0.9050414,0.06352713,0.004107169,0.02247387,0.00008567973],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1510114,0.003865869,0.5879157,0.001109782,0.0005086077,0.0006563353,0.02103017,0.2234715,0.01043064],"genre_scores_gemma":[0.3617102,0.001495666,0.5865921,0.001209307,0.0001247708,0.0005441777,0.02887955,0.00648795,0.0129562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01630756,"threshold_uncertainty_score":0.04153806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925176810484388,"score_gpt":0.3726223900011238,"score_spread":0.35337062189628,"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."}}