{"id":"W2099665608","doi":"10.1109/tmi.2009.2012899","title":"Symmetry-Based Scalable Lossless Compression of 3D Medical Image Data","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lossless compression; JPEG 2000; Computer science; Data compression; Image compression; Lossy compression; Computer vision; Artificial intelligence; Scalability; Algorithm; Wavelet transform; Wavelet; Image processing; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003703882,0.000387659,0.0003339823,0.0005901887,0.0001325705,0.0004037353,0.0006501306,0.0003855814,0.001257288],"category_scores_gemma":[0.001106759,0.0001818828,0.0003929697,0.0004881132,0.0003241375,0.0008044745,0.0006910725,0.0004814367,0.0005701027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001935643,"about_ca_system_score_gemma":0.0003204418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003954473,"about_ca_topic_score_gemma":0.0004964515,"domain_scores_codex":[0.9996849,0.00003221651,0.00001747077,0.00002182807,0.0002259333,0.00001754644],"domain_scores_gemma":[0.9996468,0.0001135749,0.00004964217,0.00009908717,0.00007146026,0.00001939763],"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":[0.0004941527,0.000116724,0.000608365,0.0002290748,0.00005855001,0.0008089439,0.0001391378,0.08886547,0.5252271,0.01179009,0.004953702,0.3667088],"study_design_scores_gemma":[0.00005225412,0.0002112264,0.001091475,0.00002090993,0.00002753872,0.001962675,0.00003211452,0.7754617,0.2121487,0.003600173,0.005352185,0.00003912993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0496183,0.0005433961,0.9472674,0.0002120626,0.00006418485,0.00008056607,0.0001821703,0.0006426654,0.001389256],"genre_scores_gemma":[0.4087312,0.001096075,0.5843642,0.0003088866,0.0001470611,0.0001575804,0.0008786679,0.0001781619,0.004138121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001257288,"threshold_uncertainty_score":0.004206061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141983792750552,"score_gpt":0.3262134527351578,"score_spread":0.3047936148076523,"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."}}