{"id":"W1538186882","doi":"10.1007/978-3-642-04271-3_134","title":"A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver Coastal Health Research Institute; University of British Columbia; Vancouver Coastal Health; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Initialization; Artificial intelligence; Segmentation; Leverage (statistics); Robustness (evolution); Pattern recognition (psychology); Market segmentation; Random walker algorithm; Image segmentation; Random forest; Computer vision; 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":[0.0004866008,0.0001049876,0.0001766371,0.0003701541,0.00009560079,0.00006238138,0.0001227874,0.00003849811,0.00001712145],"category_scores_gemma":[0.0001511037,0.00008802231,0.00004846057,0.0007417597,0.00004771251,0.00008956983,0.00003362224,0.0001094586,0.000004458825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001545433,"about_ca_system_score_gemma":0.00008092834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001600122,"about_ca_topic_score_gemma":0.00006142606,"domain_scores_codex":[0.998913,0.00002935957,0.0002352508,0.0003177987,0.0002366536,0.0002678724],"domain_scores_gemma":[0.9994846,0.0001831123,0.00004232864,0.0001845885,0.00004496387,0.00006040234],"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":[0.00007002736,0.00008815453,0.0003123558,0.00003522565,0.000002628003,0.00003146932,0.00128054,0.008538241,0.01054016,0.00001168471,0.00001605031,0.9790735],"study_design_scores_gemma":[0.003369567,0.000370956,0.01292151,0.0001537876,0.00001113831,0.00009574143,0.000009157307,0.9702982,0.01133098,0.001254427,0.00006486287,0.0001196731],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3641459,0.00001922365,0.6328759,0.002099017,0.0001666719,0.0006331707,3.545714e-7,0.00004308582,0.00001662127],"genre_scores_gemma":[0.8355999,0.000002246483,0.1618837,0.002406023,0.00007541167,0.00002081099,0.00000273689,0.000004369885,0.000004753506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9789538,"threshold_uncertainty_score":0.3589446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212712001307796,"score_gpt":0.2747998877907099,"score_spread":0.2626727677776319,"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."}}