2024
High‐resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior
Dong S, Shewarega A, Chapiro J, Cai Z, Hyder F, Coman D, Duncan J. High‐resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior. NMR In Biomedicine 2024, 37: e5145. PMID: 38488205, DOI: 10.1002/nbm.5145.Peer-Reviewed Original ResearchDeep Image PriorU-NetUnsupervised deep learning techniquesU-Net parametersDeep learning techniquesHigh-resolution ground truthU-Net architectureSuper-resolution imagingImage priorsSuper-resolutionGround truthMean absolute errorDeepSpatial resolutionPostprocessing methodDeep imagingAbsolute errorImagesAnatomical MR imagesMR spectroscopic imagingAnatomical informationSpectroscopic imagingInformationAcquisition timeError
2023
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Kucukkaya A, Zeevi T, Chai N, Raju R, Haider S, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Scientific Reports 2023, 13: 7579. PMID: 37165035, PMCID: PMC10172370, DOI: 10.1038/s41598-023-34439-7.Peer-Reviewed Original Research
2020
Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification
Zhang F, Dvornek N, Yang J, Chapiro J, Duncan J. Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification. IEEE Transactions On Medical Imaging 2020, 39: 3331-3342. PMID: 32356739, PMCID: PMC7606489, DOI: 10.1109/tmi.2020.2990625.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkClassification taskProbability calibrationTissue classification tasksImage representationBaseline methodsPublic datasetsModel performanceRandom forest modelNetworkBetter performanceForest modelDatasetClassificationTaskCT imagesImagesOriginal model outputMR imagesModel outputInstitutional datasetPerformanceEmbeddingOutput
2018
Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework
Zhang F, Yang J, Nezami N, Laage-gaupp F, Chapiro J, De Lin M, Duncan J. Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework. Lecture Notes In Computer Science 2018, 11075: 59-66. PMID: 32432233, PMCID: PMC7236808, DOI: 10.1007/978-3-030-00500-9_7.Peer-Reviewed Original ResearchNeural networkNovel deep convolutional neural networkStandard neural network approachesTraining frameworkDeep convolutional neural networkU-Net-like architectureTissue classificationConvolutional neural networkDeep neural networksNeural network approachSegmentation masksBenchmark methodsNetwork approachPatch-based strategyLearning spacesLiver tissue classificationMagnetic resonance imagesPromising resultsNetworkImagesPredictive modelClassificationFrameworkResonance imagesArchitecture