{"id":"W2000218629","doi":"10.1007/s10044-006-0023-0","title":"A new approach for breast skin-line estimation in mammograms","year":2006,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"AI in cancer detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Line (geometry); Thresholding; Pixel; Artificial intelligence; Boundary (topology); Standard deviation; Computer science; Ground truth; Pattern recognition (psychology); Euclidean distance; Enhanced Data Rates for GSM Evolution; Mathematics; Computer vision; Image (mathematics); Statistics; Geometry; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001169282,0.00007389422,0.0001228113,0.0002069318,0.00007506734,0.0001081704,0.0001849799,0.00003244232,0.000007146929],"category_scores_gemma":[9.601315e-7,0.00007293262,0.00006926896,0.001185928,0.00001173237,0.0001199798,0.00003490314,0.00004005561,0.000004666676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003457059,"about_ca_system_score_gemma":0.00001311767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001629066,"about_ca_topic_score_gemma":0.0006930201,"domain_scores_codex":[0.9992691,0.0000116792,0.0001867042,0.000327263,0.00009164571,0.0001136524],"domain_scores_gemma":[0.9995319,0.00002651727,0.00007743578,0.0002951523,0.00003160962,0.00003742585],"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":[9.572065e-7,0.00005180454,0.007946063,0.000008815132,0.00003171856,4.519883e-8,0.0000262148,0.01448618,0.00005661791,0.001796808,0.00007563821,0.9755191],"study_design_scores_gemma":[0.0001862635,0.000005926938,0.06402218,0.000001755983,0.00008660383,0.000003447768,0.000007331704,0.9303793,0.0002080585,0.00447248,0.0005317588,0.00009491246],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000939812,0.00003672159,0.9980251,0.0004390515,0.000006794536,0.0002755359,0.0000125698,0.000044829,0.0002195631],"genre_scores_gemma":[0.8434645,0.000005339221,0.1557608,0.00005047687,0.00007865959,0.0004126358,0.00007266324,0.000004410675,0.0001504729],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9754242,"threshold_uncertainty_score":0.2974106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01149908097912117,"score_gpt":0.2594957440866683,"score_spread":0.2479966631075471,"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."}}