{"id":"W3091106624","doi":"10.3390/app10228298","title":"Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review","year":2020,"lang":"en","type":"review","venue":"Applied Sciences","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mammography; CAD; Computer science; Breast cancer; Deep learning; Breast imaging; Breast tumor; Artificial intelligence; Computer-aided diagnosis; Feature extraction; Process (computing); Medical physics; Medicine; Cancer; Engineering drawing; Engineering","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":[0.001609346,0.0007717222,0.001059003,0.003131205,0.0002577709,0.0009498888,0.001053063,0.001397595,0.003147469],"category_scores_gemma":[0.003487737,0.0004585815,0.00082469,0.002719037,0.0005366133,0.001823575,0.0006106116,0.001225405,0.001529729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008036071,"about_ca_system_score_gemma":0.001978274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001818235,"about_ca_topic_score_gemma":0.002376853,"domain_scores_codex":[0.9995535,0.0001015222,0.00009561209,0.00006126951,0.0001606561,0.00002753187],"domain_scores_gemma":[0.9973754,0.001755491,0.0001406174,0.0000464281,0.0006361824,0.00004587171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00004599036,0.00004470204,0.000273255,0.0298859,0.0001618586,0.0001389014,0.00007595469,0.000599703,0.0005747931,0.003424247,0.01793781,0.9468369],"study_design_scores_gemma":[0.00002368083,0.00026693,0.001961168,0.03322927,0.0006646779,0.001623632,0.00019573,0.001101525,0.001601484,0.005121342,0.9541385,0.00007210227],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00009503154,0.9983795,0.0005021887,0.0003944267,0.0001313904,0.000007374752,0.00001017964,0.000007296637,0.0004727193],"genre_scores_gemma":[0.000986107,0.997833,0.000600274,0.0002068269,0.0001522056,0.00001132792,0.0000195676,0.000002143957,0.0001885625],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003147469,"threshold_uncertainty_score":0.0105294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05640744200435759,"score_gpt":0.3522150990039896,"score_spread":0.295807656999632,"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."}}