{"id":"W3110138119","doi":"10.18280/ria.340516","title":"Medical Image Data Classification Using Deep Learning Based Hybrid Model with CNN and Encoder","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"AI in cancer detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Deep learning; Artificial intelligence; Encoder; Benchmark (surveying); Field (mathematics); Key (lock); Machine learning; Image (mathematics); Medical imaging; Pattern recognition (psychology); Data mining; Computer security; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004686069,0.0001542707,0.000164508,0.00006195072,0.0002294634,0.0001779298,0.001040999,0.00006134481,0.00006060273],"category_scores_gemma":[0.0002616022,0.000146455,0.00002381577,0.0004349479,0.0001452392,0.0007767263,0.000384286,0.0003515514,0.00005740551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000547916,"about_ca_system_score_gemma":0.0001705061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001955097,"about_ca_topic_score_gemma":0.000008626701,"domain_scores_codex":[0.9981432,0.00008436263,0.0003130747,0.0008014421,0.0003965017,0.0002614294],"domain_scores_gemma":[0.9986411,0.0001292115,0.0001386728,0.0007675263,0.0001047869,0.0002187324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000037924,0.00005187459,0.0002524608,0.00009273548,0.00001416045,0.00003852967,0.001255973,0.8504108,0.009397981,0.001127828,0.0001536642,0.1371661],"study_design_scores_gemma":[0.00004878892,0.00008624574,0.00001017055,0.00005305001,0.00001072284,0.00003708247,0.0001786705,0.9806409,0.01790304,0.0002112148,0.0006362915,0.0001838561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006939328,0.0001330431,0.9886817,0.003495583,0.00007387845,0.0001540534,0.000002459841,0.0001736633,0.0003462441],"genre_scores_gemma":[0.8693118,0.0000456836,0.1299898,0.0004928863,0.00008694445,0.000008090504,0.00001202216,0.00001863859,0.00003414053],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8623725,"threshold_uncertainty_score":0.5972263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1135753839031384,"score_gpt":0.306213830605673,"score_spread":0.1926384467025346,"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."}}