{"id":"W4387015870","doi":"10.1038/s41467-023-41574-2","title":"Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy","year":2023,"lang":"en","type":"article","venue":"Nature Communications","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Histology; Frozen section procedure; Gold standard (test); Concordance; Stain; Prostate; Microscopy; Histopathology; Digital pathology; Pathology; Computer science; Biomedical engineering; Medicine; Radiology; Staining; Cancer","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":[0.000229714,0.0002380766,0.0002693501,0.0002191259,0.0004531424,0.00005043339,0.0006307479,0.0002789309,0.00001566851],"category_scores_gemma":[0.0003626622,0.0002360114,0.00005263848,0.0007858103,0.0002263623,0.00007775877,0.00009840389,0.001945867,0.00007692739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000186428,"about_ca_system_score_gemma":0.00005323205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000813598,"about_ca_topic_score_gemma":0.0001971239,"domain_scores_codex":[0.9987626,0.0001116207,0.0002531412,0.0002372558,0.0001668643,0.0004685166],"domain_scores_gemma":[0.9977289,0.0007629564,0.00006392258,0.001214812,0.0001287419,0.0001006063],"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":[0.0001155582,0.00009219117,0.0004806679,0.0002646121,0.0005316883,0.0002064139,0.004859899,0.4305862,0.4840982,0.001877483,0.02684125,0.0500459],"study_design_scores_gemma":[0.0004455188,0.00003898345,0.0007177463,0.0001091717,0.0001226667,0.00016799,0.0006517564,0.9668583,0.0009574092,0.0001187698,0.02945117,0.0003605003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1286202,0.009372851,0.7978346,0.002831639,0.002244591,0.001475042,0.0001486694,0.009869516,0.04760289],"genre_scores_gemma":[0.9786121,0.001132634,0.01915474,0.0001787115,0.0000503815,0.000005379955,0.0003432055,0.00008455467,0.000438257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8499919,"threshold_uncertainty_score":0.9624266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008567718946450852,"score_gpt":0.2535162283503879,"score_spread":0.244948509403937,"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."}}