{"id":"W3200172539","doi":"10.1016/j.tice.2021.101653","title":"Automatic segmentation of blood cells from microscopic slides: A comparative analysis","year":2021,"lang":"en","type":"article","venue":"Tissue and Cell","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Segmentation; Benchmark (surveying); Biology; Blood smear; Artificial intelligence; Gametocyte; Deep learning; Cell type; Computer science; Cell; Pattern recognition (psychology); Machine learning; Malaria; Immunology; Plasmodium falciparum","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":[0.002926561,0.000409171,0.000729955,0.00429086,0.0006404085,0.001450917,0.0008976306,0.0008479442,0.004397703],"category_scores_gemma":[0.003825154,0.0003568851,0.0006477988,0.001600237,0.0005928681,0.001032976,0.0006254445,0.0003206912,0.001002672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006958161,"about_ca_system_score_gemma":0.0006868087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003429725,"about_ca_topic_score_gemma":0.004181312,"domain_scores_codex":[0.9986389,0.0002798095,0.0001274547,0.0002255805,0.0005839562,0.0001442831],"domain_scores_gemma":[0.9963387,0.001548375,0.0002240364,0.0003696241,0.001410858,0.000108365],"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":[0.002436891,0.0002682511,0.01484689,0.0008827925,0.0003542225,0.0002853103,0.0007620009,0.003721287,0.6617309,0.001882705,0.001659192,0.3111696],"study_design_scores_gemma":[0.0001594273,0.001393236,0.2859125,0.0001688588,0.001263215,0.006240682,0.001558075,0.1379481,0.5398706,0.002800451,0.02252987,0.0001549022],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7698572,0.006745515,0.2120673,0.0002581689,0.0001741201,0.0003118703,0.0009048613,0.00141226,0.008268707],"genre_scores_gemma":[0.8091305,0.002202332,0.1808505,0.00007211063,0.0000693345,0.0001240907,0.001910233,0.0005555799,0.005085284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004397703,"threshold_uncertainty_score":0.0154773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110962736779869,"score_gpt":0.2592765626802589,"score_spread":0.2481669353124602,"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."}}