{"id":"W4404276110","doi":"10.3390/s24227231","title":"Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Skin cancer; Deep learning; Computer science; Artificial intelligence; Leverage (statistics); Artificial neural network; Deep neural networks; Architecture; Test set; Cancer; Machine learning; Medicine","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":[0.0004989661,0.0007878611,0.0004757599,0.0004869173,0.0002633772,0.000516823,0.001324144,0.0006040271,0.002267929],"category_scores_gemma":[0.001250323,0.0002562765,0.0004530436,0.0003496159,0.0003841709,0.001232998,0.0009484419,0.001116134,0.0007163901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008558909,"about_ca_system_score_gemma":0.0008680422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007665903,"about_ca_topic_score_gemma":0.01797746,"domain_scores_codex":[0.9998327,0.00002515366,0.000006383797,0.000052345,0.00004309381,0.00004012405],"domain_scores_gemma":[0.9997255,0.00009296495,0.00002089627,0.00004402453,0.00008380738,0.00003280846],"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":[0.0004114258,0.0003339921,0.003878858,0.0001674982,0.0001874959,0.0002808563,0.00016225,0.3002658,0.05818843,0.005724518,0.01182085,0.618578],"study_design_scores_gemma":[0.00001697139,0.00008837363,0.0004673984,0.000008119808,0.00002607081,0.00004028993,0.00001258079,0.9831761,0.01255864,0.002228732,0.001367588,0.000009078052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1720882,0.00185903,0.8093544,0.0009584542,0.0002822566,0.0002175789,0.0004286017,0.008636538,0.006174978],"genre_scores_gemma":[0.8136562,0.0004689667,0.1762018,0.0007292407,0.0000997634,0.00012141,0.0008607605,0.0002536113,0.007608249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007665903,"threshold_uncertainty_score":0.01524258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02110727035228062,"score_gpt":0.2884575139501276,"score_spread":0.267350243597847,"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."}}