2022
Robust convolutional neural networks against adversarial attacks on medical images
Shi X, Peng Y, Chen Q, Keenan T, Thavikulwat A, Lee S, Tang Y, Chew E, Summers R, Lu Z. Robust convolutional neural networks against adversarial attacks on medical images. Pattern Recognition 2022, 132: 108923. DOI: 10.1016/j.patcog.2022.108923.Peer-Reviewed Original ResearchConvolutional neural networkMedical imagesAdversarial attacksAdversarial perturbationsNeural networkRobust convolutional neural networkNovel defense methodMedical image modalitiesReal-world scenariosSignificant security risksDefense methodsFeature representationSecurity risksHuman expertsNoisy featuresAttacking methodImage modalitiesAttacksImagesNetworkMedical applicationsOriginal performanceSparsityPerformance deteriorationApplicationsMulti-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS.
Ghahramani G, Brendel M, Lin M, Chen Q, Keenan T, Chen K, Chew E, Lu Z, Peng Y, Wang F. Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS. AMIA Annual Symposium Proceedings 2022, 2021: 506-515. PMID: 35308963.Peer-Reviewed Original ResearchConceptsAge-related macular degenerationImage featuresMulti-task learning frameworkConvolutional neural networkVision lossLate age-related macular degenerationEye Disease StudyLearning frameworkNeural networkFundus photographsPatient riskMacular degenerationStandard featuresSevere formComplex featuresSurvival analysisCurrent visitLongitudinal dataDisease StudyHistorical dataRapid paceFeaturesNetworkAREDSPatients
2021
Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study
Chen Q, Rankine A, Peng Y, Aghaarabi E, Lu Z. Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study. JMIR Medical Informatics 2021, 9: e27386. PMID: 34967748, PMCID: PMC8759018, DOI: 10.2196/27386.Peer-Reviewed Original ResearchSemantic textual similarityConvolutional neural networkDeep learning modelsReal-time applicationsDL modelsSentence pairsNeural networkTextual similarityBERT modelNational Natural Language Processing Clinical ChallengesLearning modelNatural language processingAverage Pearson correlationData setsDifferent similarity levelsInference timeGeneralization capabilityManual annotationLanguage processingPearson correlationEnsemble modelWord orderTime efficiencyNegation termsTraining set
2019
ML-Net: multi-label classification of biomedical texts with deep neural networks
Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. Journal Of The American Medical Informatics Association 2019, 26: 1279-1285. PMID: 31233120, PMCID: PMC7647240, DOI: 10.1093/jamia/ocz085.Peer-Reviewed Original ResearchConceptsMulti-label classificationML-NetBiomedical textEnd deep learning frameworkMulti-label text classificationDeep learning frameworkDeep neural networksTraditional machineDocument contextFeature engineeringText classificationTextual documentsMachine learningNovel endLearning frameworkPrediction networkIndividual classifiersNeural networkHuman effortTarget documentsF-measureArt methodsPrediction mechanismContextual informationLabel counts