{"id":"W2108739393","doi":"10.1002/tee.21883","title":"Lossless compression of mammographic images with region‐based predictor selection","year":2013,"lang":"en","type":"article","venue":"IEEJ Transactions on Electrical and Electronic Engineering","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Lossless compression; Computer science; Context (archaeology); Data compression; Lossy compression; Artificial intelligence; Compression (physics); JPEG 2000; Image compression; JPEG; Categorization; Lossless JPEG; Selection (genetic algorithm); Data compression ratio; Pattern recognition (psychology); Data mining; Image (mathematics); Image processing","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":[0.000309167,0.0003209561,0.0003269949,0.0006202154,0.0001261828,0.0002705503,0.0003832615,0.0002400375,0.0006271062],"category_scores_gemma":[0.001143481,0.0001157192,0.000166477,0.0004655338,0.0001464492,0.000354351,0.0003883426,0.0002792155,0.0002398332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001348747,"about_ca_system_score_gemma":0.0002245268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007177691,"about_ca_topic_score_gemma":0.001020928,"domain_scores_codex":[0.9998519,0.00002484306,0.000008576938,0.00002288215,0.00007976951,0.00001202894],"domain_scores_gemma":[0.9996996,0.0001192187,0.00004645852,0.00004861128,0.00007235402,0.00001378335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001269354,0.0001353326,0.003681215,0.0001509046,0.00004966273,0.0003987926,0.00009891509,0.04786909,0.2020805,0.00130443,0.001966488,0.7409953],"study_design_scores_gemma":[0.00005665077,0.0003215496,0.009601066,0.00003711339,0.00007335246,0.0008255466,0.00004349145,0.8234155,0.1618174,0.0008819121,0.002899545,0.00002687184],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4252395,0.003305549,0.5677893,0.0002474381,0.0001491114,0.00008317522,0.0002549756,0.001521434,0.001409625],"genre_scores_gemma":[0.7987398,0.0009355903,0.1979921,0.0001047861,0.00009774722,0.00005197765,0.0002819304,0.00006299363,0.001733089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007177691,"threshold_uncertainty_score":0.002097845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003629789729384259,"score_gpt":0.1842299161095685,"score_spread":0.1806001263801842,"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."}}