{"id":"W3194733243","doi":"10.1109/nss/mic42677.2020.9508078","title":"Impact of De-noising and Contrast Enhancement on Segmenting Small PET Features with Low Contrast","year":2020,"lang":"en","type":"article","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contrast (vision); Artificial intelligence; Segmentation; Contrast enhancement; Pattern recognition (psychology); Computer science; Computer vision; Image segmentation; Filter (signal processing); Magnetic resonance imaging; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009079349,0.0008635807,0.0003942547,0.0002791779,0.0002223633,0.0007624599,0.0004509799,0.0008153295,0.0006274499],"category_scores_gemma":[0.003047512,0.0003363954,0.0004446541,0.0001684771,0.0005214519,0.0005269794,0.0004913386,0.000627863,0.0001890844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000289514,"about_ca_system_score_gemma":0.0003104234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00116608,"about_ca_topic_score_gemma":0.001770681,"domain_scores_codex":[0.9997345,0.00005596119,0.00001927964,0.00007076249,0.00008570628,0.00003384798],"domain_scores_gemma":[0.9987556,0.0008442238,0.0001387111,0.0001187848,0.0001039641,0.00003882078],"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":[0.0006183471,0.0001156587,0.00188068,0.0002901384,0.00007348465,0.0004387856,0.00009382011,0.05150951,0.9136065,0.0007799141,0.0001446625,0.03044838],"study_design_scores_gemma":[0.00002543216,0.0005158739,0.003857346,0.00002211182,0.00009194153,0.0008443021,0.00003477103,0.173347,0.8190266,0.0004453379,0.001748728,0.0000406658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7226218,0.002314579,0.2712396,0.0003366695,0.00009631864,0.00009776286,0.0001166115,0.0005620063,0.002614735],"genre_scores_gemma":[0.7920843,0.0008499504,0.2052296,0.0001509668,0.00002251048,0.00003481325,0.0001714637,0.0002024825,0.001254071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00116608,"threshold_uncertainty_score":0.004801631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593946419473515,"score_gpt":0.3009892821593752,"score_spread":0.2850498179646401,"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."}}