{"id":"W4388215488","doi":"10.2139/ssrn.4617448","title":"White Blood Cell Microscopic Image Dataset for Segmentation","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"White (mutation); Segmentation; Artificial intelligence; Image (mathematics); Computer vision; Computer science; Image segmentation; Pattern recognition (psychology); Biology","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":[0.0003274235,0.001244739,0.0008088949,0.002423231,0.0004822534,0.0007384113,0.001023156,0.001148689,0.01472143],"category_scores_gemma":[0.0009843929,0.0003264769,0.0006729807,0.00179594,0.0002150615,0.0003036547,0.0007374725,0.0006003847,0.01522287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004228556,"about_ca_system_score_gemma":0.000881959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006027466,"about_ca_topic_score_gemma":0.01182582,"domain_scores_codex":[0.9997638,0.00001886194,0.00002155679,0.00007831265,0.00006818998,0.00004932983],"domain_scores_gemma":[0.9995375,0.00005347092,0.00003143648,0.0001590542,0.0001648972,0.00005367314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009786484,0.0004714034,0.005086025,0.00108427,0.0002208307,0.0005983454,0.0000919146,0.003916784,0.07999758,0.00145912,0.7339347,0.1721603],"study_design_scores_gemma":[0.0005550346,0.0003806444,0.05478656,0.000298403,0.0004187332,0.004324356,0.0002512932,0.05710815,0.1309727,0.005750827,0.7450031,0.0001502114],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05339581,0.001546425,0.04136664,0.0005478051,0.0002967349,0.0007067015,0.8726696,0.01888393,0.0105864],"genre_scores_gemma":[0.03517606,0.0005290587,0.03630752,0.0002165557,0.00006603022,0.0005838029,0.9192901,0.0009256968,0.006905078],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01472143,"threshold_uncertainty_score":0.04924804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424670233684267,"score_gpt":0.2798160557242151,"score_spread":0.2655693533873724,"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."}}