2021
Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes
Durant TJS, Dudgeon SN, McPadden J, Simpson A, Price N, Schulz WL, Torres R, Olson EM. Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes. Clinical Chemistry 2021, 68: 218-229. PMID: 34969114, DOI: 10.1093/clinchem/hvab237.Peer-Reviewed Original Research
2017
Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes
Durant T, Olson EM, Schulz WL, Torres R. Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes. Clinical Chemistry 2017, 63: 1847-1855. PMID: 28877918, DOI: 10.1373/clinchem.2017.276345.Peer-Reviewed Original ResearchMeSH KeywordsDatabases, FactualErythrocytesHumansImage Processing, Computer-AssistedNeural Networks, ComputerConceptsConvolutional neural networkNeural networkDeep convolutional neural networkDense shortcut connectionsNeural network designSlow manual processSignificant labor costsClassification of erythrocytesWeb applicationCapable machinesUnseen dataShortcut connectionsManual processEnsemble model predictionsDigital imagesPrecision metricsArchitectural considerationsNetwork designAutomated profilingClassification frequencyMisclassification errorPractical performanceFinal databaseHarmonic meanNetworkA novel network analysis tool to identify relationships between disease states and risks for red blood cell alloimmunization
Celli R, Schulz W, Hendrickson JE, Tormey CA. A novel network analysis tool to identify relationships between disease states and risks for red blood cell alloimmunization. Vox Sanguinis 2017, 112: 469-472. PMID: 28337751, DOI: 10.1111/vox.12515.Peer-Reviewed Original ResearchMeSH KeywordsAgedAllograftsErythrocyte TransfusionErythrocytesHumansIsoantibodiesMaleRisk AssessmentRisk Factors