{"id":"W3103111471","doi":"10.1117/1.jbo.25.11.116010","title":"Integrating photoacoustic tomography into a multimodal automated breast ultrasound scanner","year":2020,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scanner; Breast imaging; Imaging phantom; Computer science; Biomedical engineering; Iterative reconstruction; Materials science; Ultrasound; Tomography; Optics; Artificial intelligence; Breast cancer; Physics; Medicine; Mammography; Acoustics; Cancer","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":[0.0006830299,0.0003419671,0.0002391871,0.0003062726,0.000117584,0.0004767592,0.0009049929,0.0007276924,0.001909024],"category_scores_gemma":[0.001516192,0.0004041304,0.000269228,0.000237587,0.0002654031,0.0005365116,0.0007015674,0.0003703455,0.0004793388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004147433,"about_ca_system_score_gemma":0.0005981979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007090376,"about_ca_topic_score_gemma":0.001191561,"domain_scores_codex":[0.9996508,0.00007785362,0.00001769395,0.00007882032,0.000148535,0.0000263866],"domain_scores_gemma":[0.9993181,0.000314818,0.00008895918,0.00008019812,0.0001540063,0.00004390297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003244472,0.00008481317,0.003068238,0.00013715,0.00004366186,0.0003172627,0.0001161039,0.0153307,0.9254809,0.0005891873,0.0004338009,0.05407372],"study_design_scores_gemma":[0.00008265363,0.001089328,0.00766315,0.00005141447,0.0001415599,0.002083946,0.00006865368,0.3898261,0.5902199,0.000684803,0.007981665,0.0001067818],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.256137,0.0002147281,0.7389452,0.0002134622,0.00003294125,0.0002311077,0.0001065936,0.003198911,0.0009200552],"genre_scores_gemma":[0.3033265,0.0001044455,0.6949984,0.00011604,0.00001337032,0.0001243685,0.0000868366,0.0001225959,0.00110744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001909024,"threshold_uncertainty_score":0.00638628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005033790243897776,"score_gpt":0.2158023435232458,"score_spread":0.210768553279348,"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."}}