{"id":"W7130702121","doi":"10.1109/computingcon64838.2025.11376975","title":"A Robust and Interpretable Deep Learning-Based System for Multi-Category Skin Cancer Categorization on Resource-Constrained Edge Devices","year":2025,"lang":"","type":"article","venue":"","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Interpretability; Skin cancer; Deep learning; Inference; Generalizability theory; Feature (linguistics); Enhanced Data Rates for GSM Evolution; Convolutional neural network; Ensemble learning; Feature extraction","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":[0.0004278508,0.0008768849,0.0005854727,0.0005680927,0.0003425239,0.0006846286,0.001799254,0.0009208601,0.003013237],"category_scores_gemma":[0.00119621,0.0002974847,0.0005024871,0.0003897495,0.0002134937,0.0008094204,0.001196128,0.0009995641,0.001521204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007631541,"about_ca_system_score_gemma":0.0007935881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008202327,"about_ca_topic_score_gemma":0.01405915,"domain_scores_codex":[0.999759,0.00002768766,0.00001344752,0.00009945276,0.00005070533,0.00004968801],"domain_scores_gemma":[0.9997744,0.00005339252,0.00002423768,0.0000418738,0.00008005512,0.00002603221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005282323,0.0005661916,0.007486945,0.0001539247,0.0001254447,0.0005251174,0.0001252637,0.1234216,0.0265688,0.00238577,0.03395313,0.8041595],"study_design_scores_gemma":[0.00001540102,0.00006823143,0.0009646381,0.00001774662,0.00001770228,0.00008262935,0.00002727637,0.9887168,0.006219965,0.001804374,0.002051393,0.00001368047],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1483978,0.001300098,0.8171532,0.001055667,0.000403385,0.0003204068,0.002163147,0.02182983,0.007376444],"genre_scores_gemma":[0.7859656,0.0004031247,0.1960503,0.001053822,0.0001159678,0.0003052487,0.004483422,0.0002475756,0.01137493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008202327,"threshold_uncertainty_score":0.01630914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207182269000145,"score_gpt":0.2673463269146505,"score_spread":0.245274504224649,"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."}}