{"id":"W4413121939","doi":"10.1109/ecti-con64996.2025.11101034","title":"GlandNet: Automated Gland Segmentation for Colorectal Cancer Diagnosis using UNet","year":2025,"lang":"en","type":"article","venue":"","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Colorectal cancer; Computer science; Segmentation; Cancer; Medicine; Radiology; Artificial intelligence; Internal medicine","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":[0.0006645971,0.001301593,0.0006975411,0.001405006,0.0003225835,0.001175437,0.001409938,0.00156111,0.005793442],"category_scores_gemma":[0.00171791,0.0004134852,0.0009414958,0.0007510504,0.0002827996,0.0009398544,0.001016897,0.0005787492,0.002732675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008928516,"about_ca_system_score_gemma":0.001200182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007797388,"about_ca_topic_score_gemma":0.01869633,"domain_scores_codex":[0.9996841,0.00003962431,0.00002132969,0.0001232321,0.00007863832,0.00005310668],"domain_scores_gemma":[0.9996896,0.00008678058,0.00003010149,0.00007019708,0.00008666256,0.00003665599],"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":[0.001782385,0.000434387,0.02573308,0.001295516,0.0006311602,0.001220468,0.0002066543,0.06155928,0.06466706,0.003367748,0.1012774,0.7378249],"study_design_scores_gemma":[0.0001664284,0.000569238,0.01232977,0.0001660248,0.0002178813,0.002431224,0.0001828378,0.8666159,0.06725418,0.004576575,0.04540858,0.00008128455],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3155249,0.009475088,0.5130144,0.002123581,0.001123462,0.00107763,0.0268569,0.1084727,0.02233136],"genre_scores_gemma":[0.5176741,0.002133739,0.4032803,0.001167691,0.0002229896,0.0003935063,0.05502746,0.00251774,0.01758247],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007797388,"threshold_uncertainty_score":0.01938105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02527521341926137,"score_gpt":0.3519717177757409,"score_spread":0.3266965043564796,"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."}}