{"id":"W2771942883","doi":"10.1109/smc.2017.8122605","title":"Medical image compression based on region of interest using better portable graphics (BPG)","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Lossy compression; Computer science; Lossless compression; Region of interest; Image compression; Computer vision; Data compression; Artificial intelligence; Medical imaging; Graphics; Compression (physics); Texture compression; Image processing; Computer graphics (images); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003027474,0.0001602238,0.0002379907,0.0001665736,0.0002755039,0.0001391364,0.002487853,0.0001244835,0.00007244877],"category_scores_gemma":[0.0002281339,0.0001218397,0.00007460015,0.00008474303,0.0002281475,0.001080411,0.001075668,0.0002665886,0.000004837929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001843405,"about_ca_system_score_gemma":0.00006097101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001085417,"about_ca_topic_score_gemma":0.00001159309,"domain_scores_codex":[0.9984654,0.00007218545,0.0003331483,0.0004218227,0.0004797392,0.0002276891],"domain_scores_gemma":[0.9968873,0.0001271244,0.0003871967,0.002343728,0.0001072568,0.0001473627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003248565,0.002322922,0.03013531,0.0004473015,0.00006970695,0.001858778,0.000170746,0.0002346114,0.1375754,0.3819183,0.1633973,0.2815448],"study_design_scores_gemma":[0.0006301605,0.0001481714,0.003039401,0.001076543,0.00000588889,0.00003002557,0.000003448542,0.7648207,0.2135974,0.01262782,0.003697627,0.0003228675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01206987,0.000007952064,0.9836593,0.001550824,0.0001899472,0.0001273389,0.000003329218,0.0002178185,0.002173625],"genre_scores_gemma":[0.7015129,0.000008639048,0.2973363,0.001047443,0.00003414474,0.000004589529,0.000004867829,0.00001165449,0.000039496],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7645861,"threshold_uncertainty_score":0.4968477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09125776043680382,"score_gpt":0.3528206759213247,"score_spread":0.2615629154845208,"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."}}