{"id":"W4410774918","doi":"10.1016/j.procs.2025.03.180","title":"An Analysis of YOLOvX Deep Learning Models for Colon Cancer Detection","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"University Grants Commission","keywords":"Computer science; Deep learning; Artificial intelligence; Cancer; Machine learning; Medicine; 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":[0.001341606,0.0007200018,0.0005683353,0.0006431944,0.0002663123,0.0007143677,0.0008087889,0.000666265,0.001447387],"category_scores_gemma":[0.003345119,0.0002587287,0.0004732785,0.0003457586,0.0002478572,0.00068335,0.0005842625,0.0007012205,0.0002923599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331791,"about_ca_system_score_gemma":0.001240863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02466646,"about_ca_topic_score_gemma":0.01666466,"domain_scores_codex":[0.9996589,0.0000886147,0.00002028139,0.0000648212,0.0001022506,0.00006506281],"domain_scores_gemma":[0.9989819,0.0005706198,0.0000772233,0.00006319176,0.0002625864,0.00004455379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000686647,0.0001566641,0.01540599,0.0002124818,0.0001568575,0.000179043,0.00005847263,0.8554842,0.004818912,0.004477116,0.004767129,0.1135966],"study_design_scores_gemma":[0.000004432845,0.00004193851,0.0007204629,0.000009243074,0.000008423599,0.00001441216,0.000005840568,0.9978638,0.000698581,0.0003634561,0.0002663491,0.000002984658],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7882577,0.00960689,0.1847555,0.00227016,0.0002651509,0.0001323885,0.001423581,0.002362785,0.01092572],"genre_scores_gemma":[0.9737626,0.001004809,0.01940434,0.0002144261,0.00003551463,0.00004466933,0.001890235,0.00006300028,0.00358046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02466646,"threshold_uncertainty_score":0.04904574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266476101190699,"score_gpt":0.3239127252947661,"score_spread":0.3112479642828591,"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."}}