{"id":"W2592740045","doi":"10.1117/12.2254320","title":"Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"AI in cancer detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Breast tissue; Convolutional neural network; Breast density; Segmentation; Ground truth; Pattern recognition (psychology); Contrast (vision); Breast imaging; Deep learning; Robustness (evolution); Tomography; Mammography; Computer vision; Breast cancer; Radiology; Medicine","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":[0.0008392718,0.0003024622,0.0004402113,0.0001626225,0.0001433368,0.0002889779,0.001569739,0.0001090489,0.000005165407],"category_scores_gemma":[0.0003123184,0.0002732562,0.0002803606,0.0002417767,0.0002442389,0.001320478,0.0002498475,0.0003042128,0.000001056796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004114536,"about_ca_system_score_gemma":0.00006851164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006348544,"about_ca_topic_score_gemma":0.000001549258,"domain_scores_codex":[0.9976481,4.883651e-8,0.0006500146,0.0004917245,0.0008609952,0.0003490993],"domain_scores_gemma":[0.997494,0.0001256143,0.0007897235,0.0001575605,0.001339022,0.00009410382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008160454,0.0002393942,0.005938851,0.0004952929,0.0001649199,5.946359e-7,0.0001499972,0.0009784146,0.9620779,0.02663481,0.0003514247,0.002886795],"study_design_scores_gemma":[0.001216335,0.0002540879,0.07570291,0.0003324544,0.00004127434,0.00001336068,0.0001203492,0.6049742,0.3169066,0.0002074744,0.00001610856,0.0002148452],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862274,0.00000752136,0.009508981,0.001597433,0.0002814429,0.0006723087,0.0000258082,0.0001374397,0.001541686],"genre_scores_gemma":[0.7274535,0.000009524017,0.2722546,0.00004075782,0.00009681201,0.0000980979,0.000002920078,0.00002588422,0.00001793167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6451713,"threshold_uncertainty_score":0.999972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00762455197650479,"score_gpt":0.2643549811413109,"score_spread":0.2567304291648061,"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."}}