{"id":"W2903631572","doi":"10.1093/ilar/ily007","title":"Digital Microscopy, Image Analysis, and Virtual Slide Repository","year":2018,"lang":"en","type":"article","venue":"ILAR Journal","topic":"AI in cancer detection","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Virtual microscopy; Digitization; Workflow; Digital pathology; Computer science; Telepathology; Digital imaging; Image analysis; Digital image analysis; Digital image; Microscopy; Automation; Computer vision; Artificial intelligence; Image processing; Pathology; Medicine; Image (mathematics); Engineering","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002334532,0.00008949931,0.0001204501,0.0001903091,0.0003548644,0.001152652,0.0003700364,0.00004270541,0.00001402279],"category_scores_gemma":[0.00002717928,0.00008072514,0.00008278672,0.000417499,0.0001301399,0.00125508,0.0001467123,0.000215902,0.00003055344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007562285,"about_ca_system_score_gemma":0.00006374611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005888947,"about_ca_topic_score_gemma":0.00000607079,"domain_scores_codex":[0.9991061,0.00004408514,0.0002064902,0.000225182,0.0002315767,0.0001865712],"domain_scores_gemma":[0.9992963,0.00002944293,0.0001395186,0.0002677277,0.0001379041,0.0001291201],"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":[0.0001066106,0.000151304,0.05768783,0.00001264129,0.001351185,0.0006362983,0.003318062,0.00003420709,0.5910997,0.0007358402,0.02253434,0.3223319],"study_design_scores_gemma":[0.00192257,0.00242872,0.1406519,0.00009831088,0.0004953783,0.01282803,0.0003464449,0.03201515,0.7189046,0.003680889,0.08534924,0.001278867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2318744,0.0001550961,0.7644094,0.0002163382,0.0008837499,0.00002542019,0.000001509742,0.00004971847,0.00238441],"genre_scores_gemma":[0.9768361,0.00002313224,0.02149709,0.0001330048,0.0007905953,8.217697e-7,2.92758e-7,0.000007087354,0.0007119213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7449617,"threshold_uncertainty_score":0.9998842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003959413196593032,"score_gpt":0.2381349684765753,"score_spread":0.2341755552799823,"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."}}