{"id":"W3168184531","doi":"10.1109/tim.2021.3085956","title":"A Diagnostic Biomarker for Breast Cancer Screening <i>via</i> Hilbert Embedded Deep Low-Rank Matrix Approximation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Breast cancer; Rank (graph theory); Computer science; Mathematics; Cancer; Medicine; Combinatorics; Internal 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":[0.0007342314,0.0007546699,0.000512327,0.0006104166,0.0001534525,0.0005374462,0.0004190079,0.0005302845,0.0008728848],"category_scores_gemma":[0.002138843,0.000192678,0.0006071221,0.000367649,0.0003329063,0.0005845117,0.0005078563,0.0006988495,0.0004253636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002750178,"about_ca_system_score_gemma":0.0005074407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001423034,"about_ca_topic_score_gemma":0.001854246,"domain_scores_codex":[0.9997427,0.00008597722,0.00001478761,0.00005602188,0.00005962236,0.00004091112],"domain_scores_gemma":[0.9996386,0.0001338455,0.00007958707,0.00003178879,0.00008956667,0.0000265545],"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":[0.0005979193,0.0003475204,0.01672057,0.0003835783,0.0002645907,0.0003961299,0.0001895898,0.2490658,0.09307016,0.01351262,0.01046205,0.6149895],"study_design_scores_gemma":[0.0000083767,0.00009083362,0.002235835,0.00001961032,0.00003494881,0.0001482706,0.00002172966,0.9833248,0.008193559,0.00453516,0.001366514,0.00002034881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05467556,0.0009418114,0.9417967,0.0005880046,0.00006213945,0.00003961541,0.0003152477,0.000680178,0.0009007436],"genre_scores_gemma":[0.7532064,0.001048055,0.240917,0.0003893634,0.000146769,0.0001300699,0.00105346,0.00009298035,0.003015855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001423034,"threshold_uncertainty_score":0.003883004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02782245041002921,"score_gpt":0.290972111596892,"score_spread":0.2631496611868627,"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."}}