{"id":"W4386274114","doi":"10.1109/iri58017.2023.00054","title":"Cost Efficient Mammogram Segmentation and Classification with NeuroMem® Chip for Breast Cancer Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Artificial neural network; Breast cancer; Pattern recognition (psychology); Mammography; Feature extraction; Image segmentation; Chip; Anomaly detection; Computer vision; Cancer; Medicine; Telecommunications","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":[0.0001853844,0.0003001101,0.0002708299,0.0004776081,0.0001622839,0.0002784867,0.0005307096,0.0003876812,0.002244775],"category_scores_gemma":[0.0005462432,0.0001643672,0.0001567005,0.0002708324,0.0001256539,0.0003942307,0.0002661623,0.0001484221,0.0006653655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000343017,"about_ca_system_score_gemma":0.0002930606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007111663,"about_ca_topic_score_gemma":0.002308744,"domain_scores_codex":[0.9998208,0.00003303391,0.00000920512,0.000046209,0.00007280727,0.00001798762],"domain_scores_gemma":[0.9998068,0.00007585598,0.00002823566,0.00002493681,0.00005339273,0.00001080787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005576373,0.0001123484,0.007155443,0.0002693982,0.00007207472,0.0003344766,0.00006797787,0.005923647,0.510313,0.001906345,0.003235639,0.4700519],"study_design_scores_gemma":[0.00007356,0.001175264,0.03930104,0.00006877889,0.0001832612,0.002938341,0.0001126384,0.2859019,0.645855,0.001696073,0.02261851,0.00007561228],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3939981,0.002091219,0.5924765,0.0006081695,0.0001698995,0.0001615687,0.0004695844,0.005222531,0.004802362],"genre_scores_gemma":[0.7532551,0.0003745084,0.2396408,0.0002260844,0.00003172724,0.0001056545,0.0003415317,0.0000833131,0.005941394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002244775,"threshold_uncertainty_score":0.00750953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03850851844670616,"score_gpt":0.292496976735301,"score_spread":0.2539884582885948,"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."}}