{"id":"W2108088864","doi":"10.1007/978-3-642-59327-7_44","title":"Peripheral Thickness Correction for Volumetric Breast Density Estimation","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Imaging phantom; Computation; Mammography; Spline (mechanical); Pixel; Interpolation (computer graphics); Optics; Thin plate spline; Materials science; Mathematics; Spline interpolation; Image (mathematics); Computer vision; Computer science; Physics; Algorithm; Medicine; Breast cancer; Bilinear interpolation","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":[0.0003514684,0.0008751578,0.000518492,0.0006616521,0.0001812862,0.0007321774,0.001346823,0.0005406101,0.01683982],"category_scores_gemma":[0.001510681,0.0007338546,0.0004184291,0.001025468,0.0002459832,0.0009270802,0.0006276063,0.0009872735,0.007347068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002431408,"about_ca_system_score_gemma":0.0003177316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009309763,"about_ca_topic_score_gemma":0.002524293,"domain_scores_codex":[0.9998375,0.00001872675,0.000006733874,0.00002874971,0.0001005525,0.000007737997],"domain_scores_gemma":[0.9995502,0.0001878023,0.0000224155,0.00007359515,0.0001569029,0.00000912833],"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":[0.00006208177,0.00001132719,0.0002834859,0.0002545814,0.00002416509,0.0001114372,0.00004032197,0.007477928,0.0259937,0.009834418,0.02240805,0.9334985],"study_design_scores_gemma":[0.00003714036,0.000134711,0.006666376,0.0004227239,0.0003115922,0.01102368,0.0001278277,0.3336915,0.1653899,0.03852216,0.443522,0.000150473],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001908134,0.003677981,0.9791036,0.0001490659,0.0004495014,0.00002132807,0.000172332,0.00306376,0.01145434],"genre_scores_gemma":[0.03848239,0.006905287,0.8477142,0.0002805631,0.0002915996,0.00004336451,0.0008534165,0.002852789,0.1025764],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01683982,"threshold_uncertainty_score":0.05633485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01258371246216024,"score_gpt":0.239829606877287,"score_spread":0.2272458944151268,"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."}}