{"id":"W1619780310","doi":"10.1109/icacci.2015.7275938","title":"Small bowel image classification using dual tree complex wavelet-based cross co-occurrence features and canonical discriminant analysis","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Complex wavelet transform; Pattern recognition (psychology); Artificial intelligence; Contextual image classification; Computer science; Mathematics; Wavelet transform; Wavelet; Image (mathematics); Discrete wavelet transform","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":[0.0006966733,0.00055107,0.001043927,0.00318727,0.0002345657,0.0009783589,0.000562756,0.0006214879,0.00108228],"category_scores_gemma":[0.001881045,0.000193796,0.0006924968,0.001471058,0.0002725264,0.0009505257,0.0005030063,0.0005320918,0.0008291692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002694565,"about_ca_system_score_gemma":0.0004593418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001604798,"about_ca_topic_score_gemma":0.001532009,"domain_scores_codex":[0.9993931,0.00007034827,0.00004957612,0.0001546078,0.0002640986,0.00006826963],"domain_scores_gemma":[0.9992706,0.0001596065,0.00008638378,0.00009974586,0.0003304205,0.0000532093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004995841,0.0001881991,0.00603076,0.0001272812,0.00009090123,0.0002305888,0.00004764381,0.008057472,0.06967288,0.001424814,0.003647724,0.9099823],"study_design_scores_gemma":[0.00005397957,0.0002375584,0.01586895,0.00002404062,0.0001140815,0.001019187,0.00008985787,0.9334043,0.04287136,0.001755022,0.004504748,0.00005701623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09647306,0.0008025948,0.8992371,0.0001993146,0.0001402239,0.0001369647,0.0001986938,0.001589489,0.001222522],"genre_scores_gemma":[0.4010283,0.0006527707,0.5944864,0.0001129784,0.0001396608,0.0001649034,0.0009082604,0.0001572033,0.002349572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00318727,"threshold_uncertainty_score":0.003684402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.156308649359557,"score_gpt":0.3630952469075275,"score_spread":0.2067865975479705,"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."}}