{"id":"W2020093041","doi":"10.1109/healthcom.2012.6379408","title":"An enhanced threshold based technique for white blood cells nuclei automatic segmentation","year":2012,"lang":"en","type":"article","venue":"","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Calgary Laboratory Services","keywords":"Segmentation; Computer science; Artificial intelligence; Image segmentation; Pattern recognition (psychology); Computer vision; MATLAB; Scale-space segmentation","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":[],"consensus_categories":[],"category_scores_codex":[0.0001912676,0.0001520369,0.0001219242,0.0001060889,0.00007475927,0.0003302808,0.0005679983,0.00003534044,0.00004239082],"category_scores_gemma":[0.00001327743,0.0001399524,0.00007727952,0.0002196678,0.00002908326,0.002734368,0.0000607437,0.00003474949,0.000044974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002217615,"about_ca_system_score_gemma":0.00004852682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002843381,"about_ca_topic_score_gemma":9.679072e-7,"domain_scores_codex":[0.9989173,0.00002048171,0.0001995058,0.0002745475,0.0002130537,0.0003750495],"domain_scores_gemma":[0.9990237,0.00004699142,0.00007834605,0.0005719798,0.00006490036,0.0002140845],"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":[0.000009273582,0.002198322,0.001345896,0.0001470681,0.00003306831,0.000001772062,0.0003747682,0.0002248744,0.9683914,0.01031794,0.002417847,0.01453782],"study_design_scores_gemma":[0.0004390147,0.000133038,0.0005463406,0.00002054644,0.00002894049,0.000002012519,0.00002093501,0.05113024,0.9462805,0.001119061,0.00006230053,0.0002170492],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05176221,0.00001913062,0.9414212,0.000121949,0.0001726876,0.0007878572,0.00001234685,0.0008152983,0.004887311],"genre_scores_gemma":[0.626805,1.711989e-7,0.3722526,0.0005766953,0.00002755294,0.0001921026,0.00001148797,0.00001351677,0.0001208791],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5750428,"threshold_uncertainty_score":0.5707095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224099308871274,"score_gpt":0.2630400203636059,"score_spread":0.2507990272748931,"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."}}