{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004311463,0.0006821384,0.0005798648,0.0008186525,0.0002437185,0.00069402,0.001166165,0.0008309175,0.001299551],"category_scores_gemma":[0.000901836,0.0002893004,0.0006742593,0.0006325371,0.0002385924,0.0009833899,0.0005689982,0.001125585,0.0005990421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001056149,"about_ca_system_score_gemma":0.0008157281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01576786,"about_ca_topic_score_gemma":0.01810117,"domain_scores_codex":[0.9997788,0.00002168258,0.00001775935,0.00006914658,0.00006703279,0.0000456205],"domain_scores_gemma":[0.9997063,0.00006961763,0.00003460148,0.00003743061,0.0001330718,0.00001890043],"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":[0.0003989181,0.0004474583,0.008234246,0.0001916814,0.0002086365,0.0002249292,0.00008835774,0.2246597,0.02108148,0.002731935,0.008045134,0.7336875],"study_design_scores_gemma":[0.000005099802,0.00005583281,0.000759575,0.00001126537,0.00001730582,0.00004716058,0.00001247739,0.9923544,0.005304799,0.0006936333,0.0007317654,0.000006696938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1803139,0.003492662,0.8006982,0.001764892,0.0003373554,0.0002417133,0.001505023,0.005761975,0.005884222],"genre_scores_gemma":[0.7970636,0.001293692,0.1868595,0.0006612475,0.0001297966,0.0001691169,0.003021516,0.0000745372,0.01072687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01576786,"threshold_uncertainty_score":0.03135216,"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."}}