{"id":"W3106699321","doi":"10.1145/3388440.3415988","title":"Convolutional Neural Network Strategy for Skin Cancer Lesions Classifications and Detections","year":2020,"lang":"en","type":"article","venue":"","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Convolutional neural network; Skin cancer; Artificial intelligence; Computer science; Deep learning; Artificial neural network; Skin lesion; Heuristic; Pattern recognition (psychology); Cancer; Contextual image classification; Meta heuristic; Machine learning; Cancer detection; Image (mathematics); Dermatology; Medicine; Algorithm","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":[0.0004501741,0.0006902905,0.000490004,0.0007318381,0.0003024724,0.0004794581,0.001210753,0.0008330104,0.002483985],"category_scores_gemma":[0.0009741942,0.0002828392,0.0004567923,0.000389131,0.0003156929,0.0005455904,0.00050938,0.0006213067,0.0004197261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136204,"about_ca_system_score_gemma":0.001448578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01441234,"about_ca_topic_score_gemma":0.01397042,"domain_scores_codex":[0.9997925,0.00003094387,0.00001429663,0.0000576716,0.0000580591,0.00004649502],"domain_scores_gemma":[0.999761,0.00005476775,0.00003043787,0.00002233829,0.0001140648,0.00001743606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001613842,0.0001339424,0.002398874,0.00008139545,0.00007470229,0.0001880146,0.00005709413,0.7350621,0.0127524,0.009098388,0.002949993,0.2370418],"study_design_scores_gemma":[0.000003282292,0.00002008106,0.0002253476,0.000004585823,0.000009334849,0.00002245421,0.000004481773,0.996256,0.001835696,0.001273126,0.000342615,0.000003021612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07079738,0.0006373852,0.9197998,0.0005501367,0.00009766363,0.0001528457,0.0001955605,0.0009235463,0.006845606],"genre_scores_gemma":[0.7995414,0.0002853949,0.1911982,0.0002550339,0.0000398458,0.000188682,0.0003438333,0.00005981039,0.008087785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01441234,"threshold_uncertainty_score":0.0286569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07763706842586968,"score_gpt":0.3120146850654816,"score_spread":0.234377616639612,"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."}}