{"id":"W2000967058","doi":"10.1109/tbme.2012.2199489","title":"Quantitative Evaluation of Transform Domains for Compressive Sampling-Based Recovery of Sparsely Sampled Volumetric OCT Images","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Compressed sensing; Artificial intelligence; Wavelet; Wavelet transform; Computer vision; Iterative reconstruction; Optical coherence tomography; Sampling (signal processing); Image quality; Transformation (genetics); Computer science; Fourier transform; Pattern recognition (psychology); Coherence (philosophical gambling strategy); Similarity (geometry); Mean squared error; Image processing; Image (mathematics); Mathematics; Optics; Statistics; Filter (signal processing)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005771834,0.0002164836,0.000349369,0.0007280368,0.00005078588,0.000008708835,0.0001620654,0.0001568337,0.00008249094],"category_scores_gemma":[0.00007488219,0.0002251762,0.0002491559,0.001194038,0.000125458,0.0001817713,6.53863e-7,0.0002166473,0.000004223864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001156913,"about_ca_system_score_gemma":0.00005125294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001425658,"about_ca_topic_score_gemma":0.000003216459,"domain_scores_codex":[0.9983731,0.00002563985,0.0005231487,0.0001721332,0.0005409731,0.0003649864],"domain_scores_gemma":[0.998302,0.0009847214,0.00006903549,0.0002341051,0.0002206834,0.0001894496],"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":[0.00007616806,0.0004226554,0.000008217645,0.0003557884,0.0002491021,7.802278e-8,0.0001446846,0.8667332,0.0957525,0.0002713962,0.00003654468,0.03594966],"study_design_scores_gemma":[0.001423194,0.0003753157,0.0007414504,0.0001660299,0.0003497974,0.000001235553,0.00006299334,0.8674885,0.1286739,0.0001533232,0.0002736466,0.00029057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04847902,0.0003100906,0.9491216,0.00003005667,0.0004265325,0.0007863812,0.0005949323,0.0001684334,0.00008297984],"genre_scores_gemma":[0.9328046,0.00004185398,0.06662616,0.000004614834,0.0000323942,0.0004140071,0.00003274417,0.00004185614,0.000001736692],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8843256,"threshold_uncertainty_score":0.9182417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07141626251400437,"score_gpt":0.3034686968189951,"score_spread":0.2320524343049907,"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."}}