{"id":"W2899208051","doi":"10.1109/embc.2018.8513450","title":"Discrete Heat Kernel Smoothing in Irregular Image Domains","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; University College Dublin; McGill University; National Institute of Biomedical Imaging and Bioengineering; University of Wisconsin-Madison","keywords":"Smoothing; Heat kernel; Kernel (algebra); Computer science; Artificial intelligence; Edge-preserving smoothing; Kernel smoother; Algorithm; Graph; Pattern recognition (psychology); Representation (politics); Filter (signal processing); Mathematics; Kernel method; Computer vision; Pixel; Bilateral filter; Theoretical computer science; Radial basis function kernel; Mathematical analysis; Discrete mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000779956,0.0002882424,0.0005477283,0.0008541543,0.0002946496,0.0008968243,0.0006425725,0.0006757919,0.001141345],"category_scores_gemma":[0.004466696,0.0002525609,0.0006139354,0.001014716,0.00114535,0.001221504,0.0007223537,0.0009451921,0.0003429035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005233226,"about_ca_system_score_gemma":0.0004308238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002441851,"about_ca_topic_score_gemma":0.001400406,"domain_scores_codex":[0.9995943,0.00009817186,0.00002302513,0.00009850292,0.0001445132,0.00004148229],"domain_scores_gemma":[0.9982078,0.0008066635,0.0001260453,0.0004905147,0.0002961312,0.00007285805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002101905,0.00006056408,0.001700653,0.0001841917,0.00006927369,0.0002460446,0.000254528,0.6310114,0.0386007,0.1789244,0.002934542,0.1458036],"study_design_scores_gemma":[0.000004384558,0.00001365674,0.0003127286,0.00000317511,0.000004169526,0.00003279981,0.000009157063,0.9727313,0.003520273,0.02210686,0.001251741,0.000009867495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01823431,0.0001031911,0.9807122,0.00008608752,0.00003954153,0.00001106653,0.00003844334,0.0002501758,0.0005249336],"genre_scores_gemma":[0.6006015,0.0004424367,0.3940684,0.00009941017,0.0001327833,0.00007373509,0.0002746728,0.000248009,0.004059007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002441851,"threshold_uncertainty_score":0.004855216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009972666750853483,"score_gpt":0.292409115688175,"score_spread":0.2824364489373215,"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."}}