{"id":"W2394987826","doi":"10.1016/j.procs.2015.08.226","title":"Automatic Detection of Polyp Using Hessian Filter and HOG Features","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Japan Society for the Promotion of Science","keywords":"Computer science; Artificial intelligence; Hessian matrix; AdaBoost; Computer vision; Feature (linguistics); Endoscope; Pattern recognition (psychology); Filter (signal processing); Support vector machine; Random forest; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002658483,0.00006980334,0.0001237277,0.0001713864,0.00007417852,0.00003924097,0.00006796601,0.00003092381,0.000001374948],"category_scores_gemma":[0.00006901779,0.0000570293,0.00001867901,0.0005036256,0.0001858944,0.0001998937,0.00006993327,0.00007853832,9.953143e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006285164,"about_ca_system_score_gemma":0.0002016282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007873799,"about_ca_topic_score_gemma":0.0000117378,"domain_scores_codex":[0.9992277,0.000009391817,0.0001096058,0.000213478,0.0002980682,0.0001417337],"domain_scores_gemma":[0.9995058,0.00001532972,0.00006191317,0.0001144266,0.0001536047,0.00014897],"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":[0.0004176105,0.00006630486,0.0114299,0.0002707189,0.00001572769,0.000009071742,0.003534149,0.000141213,0.0950322,0.00003374862,0.00007986115,0.8889695],"study_design_scores_gemma":[0.0006947896,0.001726116,0.1345413,0.0001746449,0.00003002218,0.0006971905,0.00006677643,0.7274841,0.1342141,0.0001876788,0.00004979983,0.0001334944],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451869,0.0001918312,0.05384468,0.00008237955,0.0003568775,0.0001193621,3.137694e-7,0.00006806599,0.0001496147],"genre_scores_gemma":[0.9730901,0.000001352969,0.02665707,0.00008236394,0.0001508623,0.000003165389,1.724943e-7,0.000004361385,0.00001055142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.888836,"threshold_uncertainty_score":0.2325587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0343980325590157,"score_gpt":0.2847317780932471,"score_spread":0.2503337455342314,"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."}}