{"id":"W4387963612","doi":"10.48550/arxiv.2310.16161","title":"MyriadAL: Active Few Shot Learning for Histopathology","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Innovates","keywords":"Computer science; Artificial intelligence; Machine learning; Oracle; Active learning (machine learning); Encoder; Supervised learning; Data mining; Artificial neural network","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":[0.0005432793,0.000328944,0.0004166862,0.0003495219,0.0003309464,0.0001015414,0.001704248,0.0003335516,0.00001829445],"category_scores_gemma":[0.0002493599,0.0003896525,0.0002972618,0.0004474913,0.00009008717,0.0001908919,0.001972752,0.001199951,0.0001508784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002161425,"about_ca_system_score_gemma":0.0001879787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002138554,"about_ca_topic_score_gemma":0.00003021028,"domain_scores_codex":[0.9975182,0.000338713,0.0001833971,0.001393315,0.00008399166,0.0004823962],"domain_scores_gemma":[0.9981185,0.0004615589,0.0002991047,0.0008235993,0.0001512839,0.0001459516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001080589,0.00009928895,0.001986872,0.00019252,0.0001189364,0.0008293104,0.001291813,0.8560493,0.0001001072,0.1280361,0.001632606,0.009555168],"study_design_scores_gemma":[0.0005607202,0.0001608192,0.003876491,0.00007044913,0.00006147765,0.000009897184,0.00009775803,0.9483457,0.0000507862,0.03543023,0.01076055,0.000575108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04364694,0.00003205404,0.950394,0.0004936509,0.001993327,0.0003498575,0.00001337258,0.001093283,0.00198355],"genre_scores_gemma":[0.9663122,0.00007253399,0.005233064,0.00009353675,0.0002547705,0.000005579446,0.00005652853,0.00004806151,0.0279237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9451609,"threshold_uncertainty_score":0.9998555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1218922713489688,"score_gpt":0.2265979497567963,"score_spread":0.1047056784078274,"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."}}