{"id":"W2134916573","doi":"10.1109/42.887618","title":"Gradient and texture analysis for the classification of mammographic masses","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":279,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Cancer Foundation; University of Calgary","funders":"","keywords":"Mahalanobis distance; Pixel; Artificial intelligence; Pattern recognition (psychology); Receiver operating characteristic; Texture (cosmology); Computer science; Contextual image classification; Database; Computer vision; Mathematics; Image (mathematics)","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":[0.0009497631,0.000416473,0.000457537,0.003257237,0.0001632462,0.0006555692,0.0002339143,0.000286722,0.0005934202],"category_scores_gemma":[0.002986728,0.0001397637,0.0003574414,0.001376124,0.0002865676,0.0005153829,0.0002497089,0.0002630351,0.0003263866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002475223,"about_ca_system_score_gemma":0.0002647253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001491052,"about_ca_topic_score_gemma":0.00171417,"domain_scores_codex":[0.9992322,0.0001688005,0.00004820905,0.00007475983,0.0004207927,0.00005513948],"domain_scores_gemma":[0.9992421,0.0003274105,0.0001311776,0.0000529915,0.000210027,0.00003638303],"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":[0.000831674,0.0001391077,0.04356742,0.0003915373,0.0001677058,0.0003379109,0.0001124776,0.01551499,0.1239034,0.001687746,0.001397014,0.8119491],"study_design_scores_gemma":[0.00005226901,0.0009029566,0.2625366,0.00009457451,0.000263788,0.003155255,0.0002825859,0.6469955,0.07443298,0.002689524,0.008441461,0.0001524891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5602485,0.00561097,0.4281864,0.0002412923,0.00009521534,0.0002414176,0.0005190286,0.001207273,0.003649883],"genre_scores_gemma":[0.849671,0.000949202,0.1481931,0.00003117013,0.00005245555,0.00007040916,0.0003023812,0.00004462241,0.0006856355],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003257237,"threshold_uncertainty_score":0.005022943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501128075316349,"score_gpt":0.2675426122484033,"score_spread":0.2525313314952398,"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."}}