{"id":"W3028015151","doi":"10.1016/j.ins.2020.05.013","title":"A GPU-based residual network for medical image classification in smart medicine","year":2020,"lang":"en","type":"article","venue":"Information Sciences","topic":"AI in cancer detection","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Convolutional neural network; Pooling; Artificial intelligence; Deep learning; Contextual image classification; Graphics processing unit; Pattern recognition (psychology); Dropout (neural networks); Residual; Set (abstract data type); Analytics; Residual neural network; Reduction (mathematics); Machine learning; Image (mathematics); Data mining; Algorithm","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":[0.0003114417,0.0004558605,0.0005126506,0.0005085635,0.0002594191,0.000578525,0.001308943,0.0007283998,0.005701378],"category_scores_gemma":[0.0009291105,0.0002671589,0.0003729552,0.0004791983,0.0001991239,0.0005774763,0.0006503965,0.0006279175,0.0016154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004668603,"about_ca_system_score_gemma":0.0007250472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01099374,"about_ca_topic_score_gemma":0.0156103,"domain_scores_codex":[0.9998517,0.00002466263,0.000006634654,0.00003475737,0.00005753504,0.00002455957],"domain_scores_gemma":[0.9997625,0.0000581914,0.00001493003,0.00003846451,0.0001019102,0.0000240798],"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":[0.0005895521,0.0002043472,0.001821686,0.0001178607,0.0001059713,0.0001607574,0.00006849135,0.2314511,0.04912283,0.005141697,0.01198416,0.6992315],"study_design_scores_gemma":[0.000007185772,0.00002921935,0.000167114,0.000003187855,0.000007948435,0.00002634688,0.000004455072,0.9946653,0.003397697,0.0005238061,0.0011637,0.000003986324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02720972,0.0002809524,0.9660651,0.000222233,0.0001050874,0.00006440567,0.000155597,0.003177545,0.002719344],"genre_scores_gemma":[0.3945408,0.0003293847,0.5914601,0.0003509189,0.00007721791,0.0001258647,0.0006999789,0.0004553939,0.01196028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01099374,"threshold_uncertainty_score":0.02185953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0581637186802168,"score_gpt":0.3221192640737534,"score_spread":0.2639555453935366,"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."}}