{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003337195,0.0006788002,0.0009053456,0.002748246,0.00029038,0.001690415,0.001117538,0.0005152957,0.01098245],"category_scores_gemma":[0.001449195,0.0004046822,0.000569862,0.003830016,0.0003640564,0.001400168,0.0007521176,0.0006412009,0.007135296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004023257,"about_ca_system_score_gemma":0.0004119445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000886648,"about_ca_topic_score_gemma":0.001316501,"domain_scores_codex":[0.999688,0.00002162434,0.00003500744,0.00007761335,0.0001467515,0.00003103612],"domain_scores_gemma":[0.9996446,0.0001097606,0.00003679479,0.00008011201,0.0001067431,0.00002184594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005000956,0.00002141471,0.0001753992,0.0002954254,0.00001141659,0.0000569554,0.00003994081,0.0007954714,0.03848857,0.004002572,0.01852304,0.9375399],"study_design_scores_gemma":[0.00005613553,0.0003525986,0.01615117,0.0005587602,0.0001679657,0.004621385,0.0003107464,0.1155826,0.2494903,0.07034644,0.5421797,0.0001822038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01371161,0.03140063,0.9188343,0.0008626551,0.001162654,0.0002152682,0.003822932,0.007596691,0.02239317],"genre_scores_gemma":[0.08135515,0.0277058,0.8188056,0.000378844,0.001396027,0.0002979603,0.01132628,0.001285643,0.05744879],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01098245,"threshold_uncertainty_score":0.03673995,"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."}}