{"id":"W1986776367","doi":"10.1109/icaee.2013.6750344","title":"New color image enhancement method for endoscopic images","year":2013,"lang":"en","type":"article","venue":"","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; RGB color model; Color image; Color histogram; Color balance; Image gradient; Chrominance; False color; Computer science; Image texture; Binary image; Grayscale; Color space; Luminance; Image segmentation; Image processing; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002699837,0.0005568624,0.0003389552,0.0009072025,0.0002197396,0.0004123594,0.0005568815,0.0005126736,0.003132321],"category_scores_gemma":[0.000468711,0.0002796121,0.0004808577,0.000505537,0.0002910341,0.0009615226,0.00042997,0.0006073829,0.001018809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000223859,"about_ca_system_score_gemma":0.000199585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003077839,"about_ca_topic_score_gemma":0.0005433288,"domain_scores_codex":[0.9997171,0.00002534279,0.00001420734,0.00005283423,0.0001746678,0.00001591068],"domain_scores_gemma":[0.9997445,0.00005591939,0.00002800991,0.00003314587,0.0001207198,0.00001777775],"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":[0.0001486924,0.00006500416,0.0003148425,0.0004295019,0.0000364866,0.0002714452,0.00009959982,0.002270307,0.4797417,0.004662171,0.003591653,0.5083687],"study_design_scores_gemma":[0.00007620791,0.0004576401,0.002981777,0.00009190744,0.0001299917,0.006506238,0.00006482173,0.1557316,0.7277389,0.002140946,0.1039318,0.0001483402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01185678,0.001958461,0.980804,0.0001280645,0.000264767,0.00009685094,0.00003330531,0.0009530186,0.003904737],"genre_scores_gemma":[0.09989427,0.002734695,0.8825457,0.0001741502,0.000186904,0.0001340541,0.00012369,0.0001712748,0.0140353],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003132321,"threshold_uncertainty_score":0.01047862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768101528526913,"score_gpt":0.3269620553756492,"score_spread":0.3092810400903801,"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."}}