{"id":"W4320024230","doi":"10.1109/bigdata55660.2022.10020333","title":"Thick Data Techniques for Identifying Abnormality in Video Frames for Wireless Capsule Endoscopy","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Gastrointestinal Bleeding Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"NOSM University; Lakehead University","funders":"","keywords":"Computer science; Artificial intelligence; Capsule endoscopy; Abnormality; Modality (human–computer interaction); Filter (signal processing); Decision tree; Deep learning; Computer vision; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Medicine; Radiology","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":[0.0009395764,0.001525093,0.0008050377,0.002517628,0.0003381333,0.001259488,0.001005987,0.001135601,0.002621608],"category_scores_gemma":[0.004724199,0.0003496494,0.0008122787,0.001682247,0.0005141721,0.002073682,0.001436554,0.001535428,0.002038891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005182775,"about_ca_system_score_gemma":0.0005083336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002412103,"about_ca_topic_score_gemma":0.003547211,"domain_scores_codex":[0.9991678,0.000123373,0.00009514423,0.0001630951,0.0003841132,0.000066485],"domain_scores_gemma":[0.9976776,0.0006010651,0.0003460615,0.0004584233,0.0008265268,0.00009024808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007001752,0.0001949194,0.005645827,0.0003753756,0.0001235264,0.0005474265,0.0001486238,0.03420948,0.1056816,0.003903456,0.008865939,0.8396037],"study_design_scores_gemma":[0.00004393686,0.0006266122,0.01171526,0.0001868545,0.0001256578,0.001171204,0.0003272525,0.8455157,0.1006045,0.009470291,0.0300888,0.0001238793],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03202165,0.001925969,0.9601663,0.0005202111,0.0003786385,0.0001929946,0.001330772,0.001533403,0.001930133],"genre_scores_gemma":[0.2419928,0.00225419,0.7459046,0.0003730727,0.0003809446,0.0002692173,0.004016993,0.0002723873,0.004535842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002621608,"threshold_uncertainty_score":0.008770168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4424567085698399,"score_gpt":0.4287686708050186,"score_spread":0.0136880377648213,"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."}}