2025
Improving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT.
Tran A, Karam G, Zeevi D, Qureshi A, Malhotra A, Majidi S, Murthy S, Park S, Kontos D, Falcone G, Sheth K, Payabvash S. Improving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT. American Journal Of Neuroradiology 2025, ajnr.a8650. PMID: 39794133, DOI: 10.3174/ajnr.a8650.Peer-Reviewed Original ResearchFast Gradient Sign MethodDeep learning modelsRobustness of deep learning modelsAdversarial attacksAdversarial imagesAdversarial trainingSign MethodModel robustnessDeploying deep learning modelsDeep learning model performanceConvolutional neural networkImprove model robustnessAcute intracerebral hemorrhageHematoma expansionMulti-threshold segmentationReceiver operating characteristicIntracerebral hemorrhageGradient descentType attacksData perturbationNeural networkProjected GradientTraining setAntihypertensive Treatment of Acute Cerebral HemorrhageThreshold-based segmentation
2024
Efficient standardization of clinical T2‐weighted images: Phase‐conjugacy e‐CAMP with projected gradient descent
Zhang H, Elsaid N, Sun H, Tagare H, Galiana G. Efficient standardization of clinical T2‐weighted images: Phase‐conjugacy e‐CAMP with projected gradient descent. Magnetic Resonance In Medicine 2024, 92: 2723-2733. PMID: 38988054, DOI: 10.1002/mrm.30214.Peer-Reviewed Original ResearchData fidelity termSignal evolution modelFidelity termGradient descentProjected GradientEfficient algorithmVirtual conjugate coilAlgorithmObjective functionMapping errorsTunable parametersLinear constraintsSampling schemeTSE dataLong echo train lengthMapsTrain lengthVirtualTurbo spin echoEcho train lengthHigh-resolutionSchemeDataBackground phaseError range
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply