{"id":"W2689604496","doi":"10.1109/ccece.2017.7946756","title":"A deep-structural medical image classification for a Radon-based image retrieval","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Convolutional neural network; Image retrieval; Transformation (genetics); Artificial intelligence; Similarity (geometry); Image (mathematics); Domain (mathematical analysis); Contextual image classification; Deep learning; Scheme (mathematics); Pattern recognition (psychology); Data mining; Information retrieval; 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":[0.0007651338,0.0002007773,0.0002241499,0.00009965824,0.0007581831,0.0009850534,0.002546763,0.0001832507,0.0001533072],"category_scores_gemma":[0.001550616,0.0001615106,0.0001775274,0.0001459102,0.0003551229,0.001145357,0.0002058642,0.0001885408,0.00006362051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007765229,"about_ca_system_score_gemma":0.000294716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001835613,"about_ca_topic_score_gemma":0.000005802483,"domain_scores_codex":[0.9979537,0.00005786219,0.0003812618,0.0005760237,0.0006607698,0.0003704215],"domain_scores_gemma":[0.997245,0.0001862358,0.0003230689,0.001579482,0.0004389871,0.0002272785],"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":[0.0003554285,0.0002572805,0.0007783082,0.0002276587,0.00006000906,0.0000503463,0.0002562234,2.072545e-7,0.366456,0.3878775,0.006633441,0.2370476],"study_design_scores_gemma":[0.001023122,0.0001167412,0.009485118,0.00002313783,0.00001084472,0.00001643746,0.00001558502,0.6487561,0.3278191,0.009126181,0.003274192,0.0003334462],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007503158,0.00002265724,0.9772448,0.01751713,0.0002726761,0.0004847797,0.000005238877,0.0005770879,0.003125314],"genre_scores_gemma":[0.4500715,0.00001140458,0.5479808,0.0007725594,0.000188094,0.00006840962,0.00002133048,0.00002134593,0.0008645458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6487559,"threshold_uncertainty_score":0.9498892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02891038414049992,"score_gpt":0.3272978681197228,"score_spread":0.2983874839792229,"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."}}