2023
Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging
Joel M, Avesta A, Yang D, Zhou J, Omuro A, Herbst R, Krumholz H, Aneja S. Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging. Cancers 2023, 15: 1548. PMID: 36900339, PMCID: PMC10000732, DOI: 10.3390/cancers15051548.Peer-Reviewed Original ResearchAdversarial imagesDeep learning modelsDL modelsDetection modelLearning modelConvolutional neural networkDetection schemeAdversarial detectionDefense techniquesMachine learningMedical imagesAdversarial perturbationsInput imageAdversarial trainingNeural networkArt performanceMagnetic resonance imagingGradient descentPixel valuesHigh accuracyImagesBrain magnetic resonance imagingAbsence of malignancyClassificationScheme
2022
Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
Jekel L, Brim WR, von Reppert M, Staib L, Petersen G, Merkaj S, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Omuro A, Johnson MH, Chiang VL, Malhotra A, Scheffler B, Aboian MS. Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review. Cancers 2022, 14: 1369. PMID: 35326526, PMCID: PMC8946855, DOI: 10.3390/cancers14061369.Peer-Reviewed Original ResearchBrain metastasesDifferentiation of gliomasMagnetic resonance imagingEligible studiesSystematic reviewSingle-center institutionConventional magnetic resonance imagingSpecific clinical circumstancesNon-invasive differentiationQuality of reportingClinical circumstancesPoor reportingClinical practiceModel assessmentResonance imagingMetastasisStudy designGliomasTRIPOD StatementMultiple studiesExternal validationClinical translationAdherenceDifferentiationReview