{"id":"W4245914245","doi":"10.32920/ryerson.14646558","title":"Medical Image Segmentation and Classification Based on Sparse Representation and Dictionary Learnng Algorithms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sparse approximation; Pattern recognition (psychology); Artificial intelligence; Computer science; Segmentation; Representation (politics); Euclidean distance; Contextual image classification; K-SVD; 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":[0.0009121817,0.0005214717,0.0009333796,0.00155807,0.0002948464,0.001053119,0.0007550407,0.001080091,0.001192148],"category_scores_gemma":[0.002700761,0.0003461427,0.0008164902,0.001319162,0.0006903513,0.001351412,0.0008292695,0.0009860402,0.0007516125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004776118,"about_ca_system_score_gemma":0.0005554722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001202815,"about_ca_topic_score_gemma":0.001331012,"domain_scores_codex":[0.9992563,0.0001969209,0.00004406296,0.0001559888,0.0002849266,0.00006180816],"domain_scores_gemma":[0.9990392,0.0004309048,0.0001301329,0.0001399427,0.0002266434,0.0000332508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002223675,0.0001174059,0.001267271,0.0002911439,0.0001013532,0.0001334809,0.0001865826,0.1534435,0.05116016,0.02952542,0.003748352,0.7598031],"study_design_scores_gemma":[0.00001004212,0.00007330117,0.0004233138,0.00001620156,0.0000151687,0.0001586777,0.00002261581,0.9796021,0.009367659,0.008055976,0.002242313,0.00001270044],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005084771,0.0002298658,0.9937842,0.0001056658,0.00002152023,0.00002954596,0.00002374718,0.0002107745,0.0005099303],"genre_scores_gemma":[0.134599,0.0007741734,0.8615671,0.0001566363,0.0001337488,0.0001130542,0.0002973059,0.0001067602,0.002252192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00155807,"threshold_uncertainty_score":0.004824102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02799863628867272,"score_gpt":0.3033188685425672,"score_spread":0.2753202322538945,"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."}}