{"id":"W1979811710","doi":"10.1117/12.709873","title":"Implementing a large-scale multicentric study for evaluation of lossy JPEG and JPEG2000 medical image compression: challenges and rewards","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Saint-Luc; Canada Health Infoway; Fraser Health; Sunnybrook Health Science Centre","funders":"","keywords":"Lossy compression; JPEG; JPEG 2000; Computer science; Quantization (signal processing); Image compression; Lossless JPEG; Compression artifact; Compression ratio; Data compression; Discrete cosine transform; Computer vision; Lossless compression; File size; Artificial intelligence; Computer engineering; Image processing; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.03474568,0.0007768195,0.0007651815,0.001159705,0.001175664,0.0005370905,0.0008739079,0.0008823087,0.003287417],"category_scores_gemma":[0.02618684,0.0003864548,0.0005323213,0.001281114,0.001296387,0.0008130273,0.001222923,0.0004229147,0.001032558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008579641,"about_ca_system_score_gemma":0.002100311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001823515,"about_ca_topic_score_gemma":0.002497349,"domain_scores_codex":[0.965601,0.02778946,0.001766248,0.001618721,0.002488391,0.0007360626],"domain_scores_gemma":[0.9774194,0.007704424,0.00299202,0.005111296,0.005286547,0.001486286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01513505,0.01913411,0.7447603,0.001228371,0.0006191051,0.002708975,0.008941474,0.001851136,0.02831264,0.002656356,0.003597021,0.1710555],"study_design_scores_gemma":[0.002889248,0.09860364,0.8624104,0.0002081488,0.0003176446,0.003944885,0.003908294,0.002159602,0.009964005,0.0007659646,0.01468708,0.0001411366],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9480292,0.00054685,0.02273263,0.0003418581,0.00006712705,0.02260232,0.0004631117,0.00007010262,0.005146746],"genre_scores_gemma":[0.9443974,0.0003302444,0.03904527,0.0004635856,0.0001323576,0.01367973,0.0004119336,0.00005996562,0.001479461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03474568,"threshold_uncertainty_score":0.1837549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958772336520903,"score_gpt":0.2923845994864881,"score_spread":0.2727968761212791,"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."}}