{"id":"W3042143729","doi":"10.3934/ipi.2020041","title":"Nonlocal regularized CNN for image segmentation","year":2020,"lang":"en","type":"article","venue":"Inverse Problems and Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Softmax function; Computer science; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Feature (linguistics); Segmentation; Pooling; Convolution (computer science); Image segmentation; Image (mathematics); Computer vision; Artificial neural network","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":[0.00006140399,0.00008240224,0.00008245864,0.00002140787,0.000134597,0.0001139995,0.0001758519,0.00001217869,0.00000256338],"category_scores_gemma":[0.00001512481,0.00008104875,0.000027378,0.0001674972,0.00004578365,0.0006863609,0.000121333,0.00006026442,0.00001225119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001286384,"about_ca_system_score_gemma":0.00001225265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004898108,"about_ca_topic_score_gemma":9.280524e-7,"domain_scores_codex":[0.9993215,0.00001163248,0.000132438,0.0003000741,0.00007337618,0.0001609905],"domain_scores_gemma":[0.9996334,0.0000359813,0.00006027706,0.0001340527,0.00004404933,0.00009223101],"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":[0.00002243947,0.00004152035,0.00036219,0.0001532115,0.00002115613,0.000009277681,0.002907007,0.001640517,0.6143061,0.04042165,0.01123931,0.3288757],"study_design_scores_gemma":[0.0005642745,0.00001990993,0.00002283562,0.000009415166,0.000005293281,0.00000648377,0.00003710094,0.9720115,0.006655465,0.008687613,0.01186646,0.0001136165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007969807,0.00005954239,0.984719,0.01362128,0.00004218998,0.0004061016,0.000002281173,0.0001375401,0.0002150676],"genre_scores_gemma":[0.07212665,0.00003332542,0.9230172,0.004510592,0.00009705067,0.0001379498,0.00001022044,0.00001366512,0.00005332612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.970371,"threshold_uncertainty_score":0.3305072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02611435537777641,"score_gpt":0.2547765550000879,"score_spread":0.2286621996223115,"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."}}