{"id":"W4389426050","doi":"10.1002/bies.202300114","title":"From pixels to insights: Machine learning and deep learning for bioimage analysis","year":2023,"lang":"en","type":"review","venue":"BioEssays","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Deep learning; Workflow; Machine learning; Field (mathematics); Preprocessor; Leverage (statistics); Context (archaeology)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003442201,0.0005016285,0.001468819,0.0006229944,0.0001761855,0.000135023,0.0003493042,0.0004818056,0.00003157007],"category_scores_gemma":[0.0004765147,0.0004340653,0.000903615,0.0009777125,0.00006216636,0.000004865378,0.0004930004,0.0003069864,0.00003800528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003267292,"about_ca_system_score_gemma":0.00004340463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001891811,"about_ca_topic_score_gemma":0.0002683674,"domain_scores_codex":[0.9976286,0.0002365243,0.0005621989,0.001082898,0.0001547234,0.0003350771],"domain_scores_gemma":[0.9987487,0.0001398931,0.0003437529,0.0005040166,0.0001077797,0.0001559001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000207723,0.0000474568,0.0001499423,0.002190219,0.006020261,0.00001988782,0.00009668485,0.00001679392,0.006465898,0.000008190909,0.001061808,0.9839021],"study_design_scores_gemma":[0.0001034003,0.0001441754,0.000003097873,0.0003891854,0.005192036,0.000001664252,0.00004086319,0.0002666339,0.0009342318,0.00002445952,0.9923763,0.0005240074],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007048399,0.9771804,0.02159769,0.00001021617,0.00002553805,0.0005830998,0.00006369436,0.0001336703,0.0003352148],"genre_scores_gemma":[0.0001807457,0.9863377,0.003197205,0.00003923916,0.0003652303,0.0002720456,0.006490882,0.0001301758,0.002986842],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9913144,"threshold_uncertainty_score":0.9998111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0265973739377801,"score_gpt":0.3416959277168508,"score_spread":0.3150985537790707,"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."}}