{"id":"W4321439049","doi":"10.1016/j.aml.2023.108627","title":"A nonlinear anisotropic diffusion model with non-standard growth for image segmentation","year":2023,"lang":"en","type":"article","venue":"Applied Mathematics Letters","topic":"Advanced Mathematical Modeling in Engineering","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Science Foundation of Heilongjiang Province; Basic and Applied Basic Research Foundation of Guangdong Province; China Postdoctoral Science Foundation; Postdoctoral Scientific Research Development Fund of Heilongjiang Province; National Natural Science Foundation of China; Heilongjiang Postdoctoral Science Foundation","keywords":"Mathematics; Anisotropic diffusion; Uniqueness; Regularization (linguistics); Scalar (mathematics); Image segmentation; Anisotropy; Level set method; Mathematical analysis; Algorithm; Segmentation; Applied mathematics; Artificial intelligence; Image (mathematics); Computer science; Geometry; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001729892,0.0002236461,0.0002646583,0.0001321677,0.0001220002,0.0001015075,0.0004450754,0.00004401627,9.258939e-7],"category_scores_gemma":[0.00003019975,0.0001877967,0.00005337158,0.0003580288,0.00003858855,0.0002382296,0.0001495295,0.0001046756,0.00003739915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006856002,"about_ca_system_score_gemma":0.00002060671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.97969e-7,"about_ca_topic_score_gemma":2.294102e-7,"domain_scores_codex":[0.9986092,0.000002598651,0.0002933639,0.0003529875,0.0003615103,0.0003803222],"domain_scores_gemma":[0.9991076,0.0001973428,0.0001075365,0.0004482607,0.00006159976,0.00007767043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002520189,0.0001206872,0.000001462146,0.001228771,0.00004981954,0.00001377544,0.003288547,0.2189717,0.5540486,0.219883,0.001232497,0.001135989],"study_design_scores_gemma":[0.0006612788,0.00003901993,9.714307e-7,0.00005715937,0.00001605634,0.000003950802,0.00006132524,0.9318684,0.01826502,0.04879181,0.00000845016,0.0002266022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0375778,9.499346e-7,0.9601415,0.0007222151,0.0000434684,0.0007032448,0.000007289334,0.0005517571,0.0002518189],"genre_scores_gemma":[0.0224797,0.000004151408,0.9767172,0.0003512465,0.00003736566,0.0003151626,0.00001265386,0.00005537147,0.00002719626],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7128966,"threshold_uncertainty_score":0.7658128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01154182178942409,"score_gpt":0.2386429713528607,"score_spread":0.2271011495634367,"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."}}