{"id":"W4298009622","doi":"10.18280/ts.390433","title":"Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cuckoo search; Masking (illustration); Computer science; Cuckoo; Wavelet; Image enhancement; Artificial intelligence; Algorithm; Contrast (vision); Contrast enhancement; Pattern recognition (psychology); Image (mathematics); Magnetic resonance imaging","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00162463,0.0002850633,0.0002707467,0.0002385788,0.0006607554,0.0002673632,0.001445928,0.00003332104,0.001993062],"category_scores_gemma":[0.00001294724,0.0003115501,0.0001689932,0.0003999076,0.0001057493,0.0005629688,0.0007445857,0.0002584375,0.00003260955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003308126,"about_ca_system_score_gemma":0.0001841873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001259204,"about_ca_topic_score_gemma":0.000002364456,"domain_scores_codex":[0.9967641,0.0001829801,0.0005420896,0.0007319357,0.0009931527,0.0007857996],"domain_scores_gemma":[0.9989061,0.0001482304,0.0001594175,0.0005128033,0.0001512597,0.0001222308],"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":[0.0001984081,0.001194061,0.0001546442,0.000188245,0.00020869,0.0001419541,0.003634952,0.0006204004,0.7570419,0.07623085,0.03423713,0.1261488],"study_design_scores_gemma":[0.002646677,0.001810517,0.0001050306,0.00003932862,0.00003514436,0.00003824309,0.0004041965,0.1657201,0.7744966,0.002923401,0.05095739,0.0008233121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005878505,0.00006474432,0.9869263,0.00182484,0.0002623239,0.001552792,0.0000330722,0.0004620662,0.002995385],"genre_scores_gemma":[0.719414,0.000009282559,0.2772443,0.0009140919,0.000131703,0.001417227,0.00004524155,0.00002961533,0.0007945229],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7135355,"threshold_uncertainty_score":0.9999337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01856199828693478,"score_gpt":0.263309734205977,"score_spread":0.2447477359190423,"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."}}