{"id":"W4401073399","doi":"10.1109/memea60663.2024.10596808","title":"Overlapping Cervical Cell Region Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Image segmentation; Computer vision","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":[0.0003341119,0.0005870081,0.0005531824,0.001529622,0.0003844696,0.0009906706,0.001024496,0.001393028,0.003589449],"category_scores_gemma":[0.001203257,0.0003888784,0.0008046432,0.0008797307,0.0003461183,0.0007854761,0.0008993355,0.0006099671,0.001278234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007370734,"about_ca_system_score_gemma":0.001089989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006705096,"about_ca_topic_score_gemma":0.009519832,"domain_scores_codex":[0.9996878,0.00002002062,0.0000165048,0.0001021157,0.0001049245,0.00006876396],"domain_scores_gemma":[0.9997086,0.00006561427,0.00003089168,0.00006702961,0.0001022464,0.00002557771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007185354,0.0001044219,0.01400876,0.000605663,0.000161612,0.001912567,0.0004434002,0.08441634,0.2982581,0.007245374,0.009035001,0.5830902],"study_design_scores_gemma":[0.00002893112,0.0001103621,0.01418313,0.0001105922,0.0001277347,0.002206364,0.0001659625,0.7185909,0.2403373,0.006627132,0.01744224,0.00006942671],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2321018,0.003558726,0.7419637,0.0007174841,0.0001770546,0.0003674999,0.002205517,0.005310581,0.01359768],"genre_scores_gemma":[0.6747094,0.001060382,0.3129939,0.0003563029,0.00005924912,0.0001582373,0.001968374,0.0004812849,0.008212754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006705096,"threshold_uncertainty_score":0.01333213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780626068552152,"score_gpt":0.2519882484497787,"score_spread":0.2341819877642572,"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."}}