{"id":"W2138379822","doi":"10.1109/iembs.2008.4649385","title":"Color image processing and content-based image retrieval techniques for the analysis of dermatological lesions","year":2008,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Hue; Artificial intelligence; Computer science; Content-based image retrieval; Pattern recognition (psychology); Computer vision; Context (archaeology); Search engine indexing; Image retrieval; Precision and recall; Image processing; Principal component analysis; Image (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":[0.0003199736,0.0001145626,0.0002703624,0.0001769089,0.0003245977,0.00008301176,0.0004860126,0.00007154246,0.00001003516],"category_scores_gemma":[0.0002095553,0.00006667109,0.0001501137,0.001041564,0.0004381262,0.0003010819,0.0000948929,0.00007652646,6.820208e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001697576,"about_ca_system_score_gemma":0.0000861656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001072169,"about_ca_topic_score_gemma":0.000001566309,"domain_scores_codex":[0.9989901,0.00004681237,0.0003152818,0.0002723199,0.0002058341,0.000169665],"domain_scores_gemma":[0.9986085,0.0003948659,0.0001677627,0.0003306608,0.0004481098,0.00005014441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001615977,0.0003457859,0.001916038,0.0001029753,0.0002135601,0.00001717082,0.0002799713,4.574282e-7,0.9224578,0.01976717,0.000830969,0.05390647],"study_design_scores_gemma":[0.0001568421,0.00006662357,0.006396155,0.000008664492,0.0001159037,0.00001348174,0.00006907803,0.08055542,0.9120072,0.0001386381,0.0003632398,0.0001087563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01169461,0.0001518185,0.9844714,0.00270994,0.000008926813,0.0004165628,0.00001205648,0.0003381408,0.0001965389],"genre_scores_gemma":[0.5516804,0.00006270392,0.4477006,0.000357913,0.000006102512,0.00004541412,0.000004944591,0.00000466961,0.000137236],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5399858,"threshold_uncertainty_score":0.2718768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06646951176574205,"score_gpt":0.3038289068418218,"score_spread":0.2373593950760797,"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."}}