{"id":"W4212986460","doi":"10.1109/access.2022.3151830","title":"Design Guidelines for Mammogram-Based Computer-Aided Systems Using Deep Learning Techniques","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"AI in cancer detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Convolutional neural network; Feature extraction; Machine learning; Computer-aided diagnosis; Mammography; CAD; Feature (linguistics); Artificial neural network; Breast cancer; Cancer; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001362392,0.001116402,0.0004144358,0.001154119,0.0004067675,0.00161088,0.002117295,0.001792378,0.01471797],"category_scores_gemma":[0.004025731,0.00063602,0.0005384432,0.0003763866,0.000537386,0.001661556,0.001113312,0.001359154,0.008324202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007787584,"about_ca_system_score_gemma":0.001132154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002083016,"about_ca_topic_score_gemma":0.004119165,"domain_scores_codex":[0.9989054,0.0002360091,0.0001350297,0.000118625,0.0005401259,0.00006471535],"domain_scores_gemma":[0.9984465,0.0004133162,0.0001159602,0.0001102282,0.0008592788,0.00005480288],"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":[0.0001677169,0.0001632956,0.001547258,0.001885552,0.00006300071,0.0006849435,0.0003872201,0.1710316,0.04743522,0.1276274,0.03756874,0.611438],"study_design_scores_gemma":[0.00009302657,0.0002876759,0.0006961414,0.000859371,0.00006048074,0.0006793013,0.0001808624,0.589906,0.03628815,0.06029229,0.3105938,0.00006289266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001457176,0.001509456,0.9776406,0.001255923,0.0001017782,0.000470574,0.0001980082,0.00208191,0.01528457],"genre_scores_gemma":[0.07463755,0.004100556,0.8961276,0.0008458273,0.0001013462,0.001572669,0.0007054688,0.0004357855,0.02147308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01471797,"threshold_uncertainty_score":0.04923648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1936062150228185,"score_gpt":0.3889147563378393,"score_spread":0.1953085413150208,"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."}}