2023
Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach
Yu M, Peterson M, Cherukuri V, Hehnly C, Mbabazi-Kabachelor E, Mulondo R, Kaaya B, Broach J, Schiff S, Monga V. Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach. Journal Of Neural Engineering 2023, 20: 10.1088/1741-2552/acd9ee. PMID: 37253355, PMCID: PMC11099590, DOI: 10.1088/1741-2552/acd9ee.Peer-Reviewed Original Research
2022
Deep Learning Applications for Acute Stroke Management
Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy‐Cramer J, Gonzalez J, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Annals Of Neurology 2022, 92: 574-587. PMID: 35689531, DOI: 10.1002/ana.26435.Peer-Reviewed Original ResearchConceptsDeep machine learningDeep learning applicationsMedical image analysisDeep neural networksPixel-wise labelingAcute stroke managementReal-world examplesDL applicationsDL approachMachine learningLearning applicationsDL modelsNeural networkStroke managementLesion segmentationMaximal utilityImage analysisElectronic medical record dataInter-rater variabilityCause of disabilityMedical record dataRelevant clinical featuresStroke detectionAdvanced neuroimaging techniquesDecision making
2018
Deep Mr Image Super-Resolution Using Structural Priors
Cherukuri V, Guo T, Schiff S, Monga V. Deep Mr Image Super-Resolution Using Structural Priors. 2014 IEEE International Conference On Image Processing (ICIP) 2018, 00: 410-414. PMID: 30930696, PMCID: PMC6440206, DOI: 10.1109/icip.2018.8451496.Peer-Reviewed Original ResearchConvolutional neural networkImage superresolutionMR Image Super-ResolutionDeep learning methodsSuper-resolution taskBrain image databaseImage Super-ResolutionLow-rank structureImage databaseArt resultsNeural networkLearning methodsTraining imageryImage priorsCompelling stateSuper-ResolutionImage matrixImage structureStructural priorsTractable fashionDifferentiable approximationImage resolutionNetworkMagnetic resonance imagesPromising results
2016
Effects of Symmetry on the Structural Controllability of Neural Networks: A Perspective
Whalen A, Brennan S, Sauer T, Schiff S. Effects of Symmetry on the Structural Controllability of Neural Networks: A Perspective. Proceedings Of The 2010 American Control Conference 2016, 2016: 5785-5790. PMID: 29176923, PMCID: PMC5699861, DOI: 10.1109/acc.2016.7526576.Peer-Reviewed Original ResearchGroup representation theoryStructural controllabilityMan-made networksDynamical systemsOptimal actuatorRepresentation theoryEffect of symmetryControl inputExplicit symmetryCritical actuatorsEngineering systemsComplex networksSymmetryControllabilityMinimum numberActuatorsCoupling structureStructural symmetryNeural networkNetworkRecent workTheorySystemBroad interest