{"id":"W3217155945","doi":"10.18280/ts.380530","title":"Investigations of Medical Image Segmentation Methods with Inclusion Mathematical Morphological Operations","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Scientific Research Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Pattern recognition (psychology); Image segmentation; False positive paradox; Fuzzy logic; Computer vision; Scale-space segmentation; CAD; Curvelet; Artificial neural network; Computer-aided diagnosis; Medical imaging; Mathematical morphology; Sensitivity (control systems); Image (mathematics); Image processing; Wavelet transform; Wavelet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00231415,0.000113161,0.0002005445,0.00001894619,0.000413896,0.00006426505,0.0002969749,0.00008203133,0.01132057],"category_scores_gemma":[0.0005866234,0.00004220532,0.00006393118,0.0005901871,0.0003398854,0.0002019387,0.000395963,0.000169618,0.00001280949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003177377,"about_ca_system_score_gemma":0.00007127677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001709933,"about_ca_topic_score_gemma":0.00007381224,"domain_scores_codex":[0.9971141,0.0008074492,0.0003815158,0.0003196348,0.001152002,0.0002252706],"domain_scores_gemma":[0.9986922,0.0006956169,0.00006162433,0.00007577603,0.0002477273,0.0002270431],"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":[0.00001348873,0.0002594948,0.0001520425,0.00001300465,0.00001362368,0.00003382998,0.0002501084,0.0001524482,0.9534963,0.003653513,0.0001361809,0.04182595],"study_design_scores_gemma":[0.0008600415,0.0006517276,0.008207409,0.0001383508,0.00004268102,0.0001578165,0.00208072,0.03203403,0.9328587,0.02023313,0.002384297,0.0003510814],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6385588,0.00003927431,0.354393,0.006089057,0.00003242492,0.0002720146,0.00003143909,0.00003844515,0.0005455012],"genre_scores_gemma":[0.3152541,0.00001938245,0.6839156,0.0002878224,0.00008401523,0.00006913567,0.0001863606,0.0000015102,0.0001820741],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3295226,"threshold_uncertainty_score":0.9895832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08079667913596938,"score_gpt":0.4034916404789488,"score_spread":0.3226949613429794,"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."}}