{"id":"W2166699357","doi":"10.1109/ccece.2013.6567779","title":"Bleeding detection in wireless capsule endoscopy based on color features from histogram probability","year":2013,"lang":"en","type":"article","venue":"","topic":"Gastrointestinal Bleeding Diagnosis and Treatment","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial intelligence; Color histogram; Capsule endoscopy; Grayscale; Histogram; Histogram equalization; Computer vision; RGB color model; Computer science; Color space; Pattern recognition (psychology); RGB color space; Skew; Feature (linguistics); Color image; Image (mathematics); Image processing; Medicine; Radiology","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":[0.0001001955,0.0001733547,0.0002639454,0.0001103573,0.00005177887,0.00003344273,0.00004763096,0.0000712402,0.0004416742],"category_scores_gemma":[0.0001294164,0.0001282519,0.00007692169,0.0001578202,0.00004173405,0.0000606884,0.00001397578,0.0001973667,0.00008890075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004333265,"about_ca_system_score_gemma":0.00003976532,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01028155,"about_ca_topic_score_gemma":0.0007403929,"domain_scores_codex":[0.9989445,0.00002946751,0.0002081863,0.000353278,0.0002113541,0.0002532185],"domain_scores_gemma":[0.9993631,0.000158395,0.00005446008,0.0002047275,0.00008406967,0.0001352876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003783833,0.003102749,0.7671439,0.0000851579,0.00005093517,0.00005238875,0.0001141199,0.0001317844,0.2088824,0.0001547566,0.001768662,0.0181348],"study_design_scores_gemma":[0.00260223,0.001790907,0.8356906,0.0003100893,0.00006117543,0.000007801281,0.00007675169,0.007485259,0.1515868,0.0002182136,0.00004746792,0.0001226835],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944021,0.00001946021,0.0001383743,0.001414694,0.0001256936,0.0009603699,0.000003676422,0.000119054,0.002816642],"genre_scores_gemma":[0.9910478,0.000002494493,0.007964308,0.0003510831,0.00006499288,0.0003903396,0.00002094673,0.00001615933,0.000141856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06854672,"threshold_uncertainty_score":0.9963091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551990988916203,"score_gpt":0.2461959865891027,"score_spread":0.2306760766999407,"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."}}