2020
Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning
Bousabarah K, Letzen B, Tefera J, Savic L, Schobert I, Schlachter T, Staib LH, Kocher M, Chapiro J, Lin M. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology 2020, 46: 216-225. PMID: 32500237, PMCID: PMC7714704, DOI: 10.1007/s00261-020-02604-5.Peer-Reviewed Original ResearchConceptsDeep convolutional neural networkAverage false positive rateDice similarity coefficientU-NetDeep learning algorithmsConvolutional neural networkTest setMean Dice similarity coefficientRandom forest classifierDCNN methodDCNN approachDeep learningNet architectureLearning algorithmNeural networkLiver segmentationManual 3D segmentationForest classifierGround truthManual segmentationFalse positive rateCorresponding segmentationSegmentationMultiphasic contrast-enhanced MRIThresholding
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