{"id":"W4378471760","doi":"10.1364/oe.489677","title":"Improving flat fluorescence microscopy in scattering tissue through deep learning strategies","year":2023,"lang":"en","type":"article","venue":"Optics Express","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Microscope; Optics; Microscopy; Scattering; Computer science; Artificial intelligence; Lens (geology); Deep learning; Fluorescence microscope; Materials science; Computer vision; Physics; Fluorescence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001589383,0.000241473,0.0001911936,0.00008045633,0.0001296148,0.0001190888,0.0003918448,0.0001990909,0.000007281994],"category_scores_gemma":[0.00009061892,0.0002700841,0.00004377515,0.0002482547,0.0001603006,0.00003822548,0.0004049659,0.0002726394,0.00002787269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003184911,"about_ca_system_score_gemma":0.00004443813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005816433,"about_ca_topic_score_gemma":0.00002498779,"domain_scores_codex":[0.9984534,0.00004782376,0.0002736602,0.0005409099,0.0001341426,0.0005500183],"domain_scores_gemma":[0.9993578,0.0000199161,0.00009558824,0.0004160408,0.0000573165,0.00005334365],"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":[0.00002637875,0.00001205484,0.0008964777,0.00005746917,0.000005399139,0.00002254262,0.0003117502,0.001732715,0.9939942,0.00009968124,0.00007905913,0.002762217],"study_design_scores_gemma":[0.000206086,0.0001204012,0.0002107077,0.00008624663,0.000004150663,0.000007495915,0.0006390489,0.001610365,0.9940938,0.0001956918,0.002516394,0.0003096647],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8076695,0.0005612339,0.1901246,0.00004596097,0.0002041624,0.0003583584,0.000008940455,0.0002559332,0.0007713648],"genre_scores_gemma":[0.7706131,0.0008415003,0.2276204,0.00004862951,0.0001519078,0.00008271563,0.0001099389,0.00008256199,0.0004491672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03749588,"threshold_uncertainty_score":0.9999751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009943679987205585,"score_gpt":0.2983289261874733,"score_spread":0.2883852462002677,"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."}}