{"id":"W2900883576","doi":"10.1371/journal.pone.0206996","title":"Novel chromaticity similarity based color texture descriptor for digital pathology image analysis","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Digital pathology; Pattern recognition (psychology); Computer vision; Color space; Computer science; Color histogram; Histogram; Color analysis; Chromaticity; Image texture; Invariant (physics); Feature extraction; Color image; Texture (cosmology); Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001355629,0.0002022103,0.0003961539,0.0002056596,0.0001459463,0.0007324942,0.0008714907,0.00006786176,0.00002781011],"category_scores_gemma":[0.0007381557,0.0001944601,0.0002233128,0.0007869016,0.0002800275,0.001370924,0.0002465493,0.00007443841,0.00007534945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005260199,"about_ca_system_score_gemma":0.0001073386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007152335,"about_ca_topic_score_gemma":0.00001572633,"domain_scores_codex":[0.998383,0.00002040293,0.0002626825,0.0005538723,0.0003636609,0.0004163582],"domain_scores_gemma":[0.9983014,0.0001777367,0.0001278394,0.000765344,0.000431038,0.0001966533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006505918,0.1118048,0.08400065,0.002077034,0.01397698,0.0003083258,0.001949973,0.0001252406,0.7236373,0.01884039,0.02338129,0.01924735],"study_design_scores_gemma":[0.00229741,0.001044992,0.02865525,0.0001256719,0.002144793,0.000007474021,0.00002182914,0.831618,0.1291276,0.003668984,0.0003568028,0.0009311786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3545326,0.00002541282,0.6418261,0.0008083074,0.00005681185,0.0003981662,0.0006149234,0.0003580465,0.001379636],"genre_scores_gemma":[0.7602387,2.157017e-7,0.2384793,0.0009170817,0.00008869475,0.00005640365,0.00006496585,0.00001472344,0.0001399281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8314927,"threshold_uncertainty_score":0.7929854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05496688771190941,"score_gpt":0.2435624427325657,"score_spread":0.1885955550206563,"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."}}