{"id":"W4302893119","doi":"10.1007/978-3-031-01651-6_4","title":"Feature Extraction and Indexing of Mammograms","year":2013,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on biomedical engineering","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Search engine indexing; Feature extraction; Computer science; Feature (linguistics); Artificial intelligence; Database index; Pattern recognition (psychology); Information retrieval; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001644096,0.0003495654,0.0003951489,0.00046621,0.00004864603,0.00007140735,0.0004040161,0.0006744263,0.000083032],"category_scores_gemma":[0.0001582212,0.0003089835,0.0001168236,0.0001051052,0.00008877787,0.0001326185,0.0001168232,0.0007128556,0.00001824286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001325391,"about_ca_system_score_gemma":0.00003970341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001051002,"about_ca_topic_score_gemma":0.000001331662,"domain_scores_codex":[0.9984383,0.00001185829,0.0002404528,0.0004962977,0.0005609741,0.0002520897],"domain_scores_gemma":[0.9987373,0.000440227,0.0001779111,0.0004407355,0.0000438918,0.000159944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001483482,0.00002190018,0.000001010315,0.000431585,0.0002090259,0.00002596719,0.00009274832,0.0006885954,0.008563537,0.03193511,0.003033737,0.9549819],"study_design_scores_gemma":[0.0003328428,0.0004873056,0.0002605205,0.003133856,0.0001223197,0.000226377,0.000004679122,0.08913431,0.03157333,0.006477882,0.8667299,0.001516657],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001882475,0.002977091,0.9568793,0.002566221,0.002592954,0.0006014994,0.0000198034,0.0007929855,0.03338187],"genre_scores_gemma":[0.5878661,0.00379572,0.310449,0.001041419,0.005679518,0.0004879397,0.00005111601,0.0009190683,0.08971016],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9534653,"threshold_uncertainty_score":0.9999362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006978836515250166,"score_gpt":0.2014697145824254,"score_spread":0.1944908780671752,"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."}}