{"id":"W2617383746","doi":"10.15353/vsnl.v1i1.58","title":"Dermal Radiomics for Melanoma Screening","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Melanoma; Radiomics; Medicine; Cancer research; Radiology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0004455462,0.0001072449,0.0002033726,0.0001110217,0.00006724397,0.0000333398,0.0000882437,0.00004857644,0.00001605399],"category_scores_gemma":[0.0003775312,0.00008951258,0.00009656961,0.00009447343,0.00006216326,0.00007567502,0.00003173831,0.0002051427,0.00001953909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004391265,"about_ca_system_score_gemma":0.00003704307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001670977,"about_ca_topic_score_gemma":2.093994e-7,"domain_scores_codex":[0.9990894,0.00002542849,0.0001849405,0.0002080541,0.0002412133,0.0002509476],"domain_scores_gemma":[0.9993559,0.00008875624,0.00005403896,0.0001653133,0.00004794927,0.0002880167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000675615,0.0001159872,0.02383805,0.0001121944,0.0001402017,0.000229922,0.0007137276,0.001472863,0.110342,0.0005323854,0.5918479,0.2699791],"study_design_scores_gemma":[0.01104548,0.0006786365,0.01233746,0.000255998,0.0001757902,0.000543449,0.000185966,0.3099389,0.003020131,0.0001499289,0.6612359,0.0004324258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6171511,0.0001352383,0.320218,0.06107713,0.0004304771,0.0002778963,0.000002325655,0.00009894746,0.0006089061],"genre_scores_gemma":[0.8250752,0.000007606186,0.1418911,0.03166404,0.0008908327,0.00001805666,0.00005903899,0.00005239657,0.000341757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.308466,"threshold_uncertainty_score":0.3650217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477146947393374,"score_gpt":0.3191634294721881,"score_spread":0.2943919599982544,"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."}}