{"id":"W4312074763","doi":"10.1101/2022.12.06.519318","title":"Whole slide image representation in bone marrow cytology","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Juravinski Hospital; McMaster University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Bone marrow; Classifier (UML); Cytology; Computer science; Digital pathology; Representation (politics); Pathology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005049926,0.0006724137,0.0005668406,0.001511119,0.0001550669,0.0009593364,0.001226274,0.0008638395,0.002653219],"category_scores_gemma":[0.002099401,0.000237001,0.0006221442,0.0008470163,0.0003232503,0.001174195,0.0008936896,0.0005702307,0.001520209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007603137,"about_ca_system_score_gemma":0.0004187322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003064393,"about_ca_topic_score_gemma":0.002612726,"domain_scores_codex":[0.9996774,0.00004512366,0.00001858357,0.000100231,0.0001114866,0.00004712023],"domain_scores_gemma":[0.9993677,0.0001668297,0.00006852751,0.0001325163,0.0002131372,0.00005143547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004362164,0.0001460093,0.004912099,0.0002783621,0.00009523395,0.0004348831,0.0001544689,0.1366496,0.1362535,0.002822549,0.008787784,0.7090294],"study_design_scores_gemma":[0.00001321886,0.0001398622,0.003467618,0.00002030988,0.00002447033,0.0002338551,0.00008279595,0.9430795,0.04608637,0.003617036,0.003211029,0.00002394276],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3156088,0.002389084,0.6577885,0.0009323196,0.0002785681,0.0002649719,0.002296648,0.01662772,0.003813361],"genre_scores_gemma":[0.7985587,0.0005471621,0.1926728,0.0002318641,0.00007106111,0.00007786693,0.003723465,0.0002762458,0.003840811],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003064393,"threshold_uncertainty_score":0.008875847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01596219263868156,"score_gpt":0.2475360619489136,"score_spread":0.231573869310232,"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."}}