{"id":"W4311164178","doi":"10.18280/ts.390540","title":"CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Noise reduction; Non-local means; Computer vision; Noise (video); Image restoration; Image processing; Image noise; Inpainting; Speckle noise; Image quality; Pattern recognition (psychology); Image segmentation; Segmentation; Image (mathematics)","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001132931,0.0002600946,0.0002293162,0.0002001689,0.001610722,0.0002212799,0.000210892,0.00002147753,0.0003592808],"category_scores_gemma":[0.0001211646,0.0003037491,0.0002088447,0.000306734,0.00009873191,0.0007400354,0.00007905259,0.0001678669,0.000005727693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009555737,"about_ca_system_score_gemma":0.0001111001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004824474,"about_ca_topic_score_gemma":0.00000829798,"domain_scores_codex":[0.9972472,0.0003154582,0.0006592151,0.0007334343,0.0005883233,0.0004563611],"domain_scores_gemma":[0.9988362,0.0002142733,0.0005059758,0.0002162796,0.0001344732,0.00009281965],"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":[0.0003606363,0.0002505275,0.000003557439,0.0001104188,0.00001061472,0.000004603105,0.0004066609,0.007727893,0.9870211,0.0001937076,0.00004383622,0.003866371],"study_design_scores_gemma":[0.0009724146,0.0001378659,0.00002844812,0.000006974127,0.00004201681,0.00002569757,0.0004189893,0.4737173,0.524007,0.0003551119,0.00009813525,0.0001900245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4714555,0.000006072436,0.5268714,0.00005775953,0.0001838129,0.001209789,0.0001068089,0.00007032453,0.0000384864],"genre_scores_gemma":[0.9562482,0.000002097224,0.04208603,0.0006115951,0.0001448965,0.0007201694,0.00003396377,0.00005552551,0.00009748856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4847927,"threshold_uncertainty_score":0.9999415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1140293442050549,"score_gpt":0.3430655511860809,"score_spread":0.229036206981026,"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."}}