{"id":"W2025834121","doi":"10.1109/tbme.2013.2244596","title":"MSIM: Multistage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin Lesion Analysis","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Skin lesion; Parametric statistics; Nonparametric statistics; Computer vision; Lesion; Skin cancer; Monte Carlo method; Pattern recognition (psychology); Image segmentation; Parametric model; Mathematics; Dermatology; Medicine; Cancer; Statistics; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004057685,0.000532463,0.0003817085,0.0005444738,0.0002149691,0.000591617,0.0008462544,0.000561684,0.002453655],"category_scores_gemma":[0.001207129,0.0003429328,0.0009232048,0.0003897194,0.0001874516,0.000433648,0.0004474426,0.000745204,0.0008843127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005751051,"about_ca_system_score_gemma":0.0005759721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003287638,"about_ca_topic_score_gemma":0.004770054,"domain_scores_codex":[0.999786,0.00003886987,0.00001060623,0.00004382477,0.0000998948,0.00002092446],"domain_scores_gemma":[0.9996923,0.00009590163,0.00005216653,0.00006387683,0.00008140362,0.00001439007],"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":[0.0003287001,0.0001802551,0.004596296,0.000190115,0.0001090355,0.0001917161,0.0001418477,0.4155639,0.09435762,0.006171423,0.005780071,0.4723891],"study_design_scores_gemma":[0.000003584491,0.00002732468,0.001241767,0.000004834974,0.00000827942,0.0001012359,0.00000745963,0.984332,0.01194941,0.0005873853,0.001723851,0.00001300639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01087598,0.00006733893,0.9871393,0.0000511504,0.00001929767,0.00003726449,0.00008679578,0.001223069,0.0004998159],"genre_scores_gemma":[0.2585074,0.0002538787,0.7364818,0.00008261682,0.0000366526,0.0001425878,0.0005983593,0.0003999203,0.003496767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003287638,"threshold_uncertainty_score":0.008208275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421000473598249,"score_gpt":0.2438398455521235,"score_spread":0.229629840816141,"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."}}