Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-018-34300-2
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dc.titleDeep learning enables automated scoring of liver fibrosis stages
dc.contributor.authorYu Y.
dc.contributor.authorWang J.
dc.contributor.authorNg C.W.
dc.contributor.authorMa Y.
dc.contributor.authorMo S.
dc.contributor.authorFong E.L.S.
dc.contributor.authorXing J.
dc.contributor.authorSong Z.
dc.contributor.authorXie Y.
dc.contributor.authorSi K.
dc.contributor.authorWee A.
dc.contributor.authorWelsch R.E.
dc.contributor.authorSo P.T.C.
dc.contributor.authorYu H.
dc.date.accessioned2020-09-04T01:44:55Z
dc.date.available2020-09-04T01:44:55Z
dc.date.issued2018
dc.identifier.citationYu Y., Wang J., Ng C.W., Ma Y., Mo S., Fong E.L.S., Xing J., Song Z., Xie Y., Si K., Wee A., Welsch R.E., So P.T.C., Yu H. (2018). Deep learning enables automated scoring of liver fibrosis stages. Scientific Reports 8 (1) : 16016. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-018-34300-2
dc.identifier.issn2045-2322
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/174199
dc.description.abstractCurrent liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated. © 2018, The Author(s).
dc.publisherNature Publishing Group
dc.sourceUnpaywall 20200831
dc.subjectbiological marker
dc.subjectcollagen
dc.subjectalgorithm
dc.subjectanimal
dc.subjectartificial neural network
dc.subjectbiopsy
dc.subjectdiagnostic imaging
dc.subjectimage processing
dc.subjectliver cirrhosis
dc.subjectmachine learning
dc.subjectmale
dc.subjectmetabolism
dc.subjectmicroscopy
dc.subjectnuclear magnetic resonance imaging
dc.subjectpathology
dc.subjectprocedures
dc.subjectrat
dc.subjectreproducibility
dc.subjectx-ray computed tomography
dc.subjectAlgorithms
dc.subjectAnimals
dc.subjectBiomarkers
dc.subjectBiopsy
dc.subjectCollagen
dc.subjectDeep Learning
dc.subjectDiagnostic Imaging
dc.subjectImage Processing, Computer-Assisted
dc.subjectLiver Cirrhosis
dc.subjectMachine Learning
dc.subjectMagnetic Resonance Imaging
dc.subjectMale
dc.subjectMicroscopy
dc.subjectNeural Networks (Computer)
dc.subjectRats
dc.subjectReproducibility of Results
dc.subjectTomography, X-Ray Computed
dc.typeArticle
dc.contributor.departmentMECHANOBIOLOGY INSTITUTE
dc.contributor.departmentDEPT OF BIOMEDICAL ENGINEERING
dc.contributor.departmentDEPT OF PATHOLOGY
dc.contributor.departmentDEPT OF PHYSIOLOGY
dc.description.doi10.1038/s41598-018-34300-2
dc.description.sourcetitleScientific Reports
dc.description.volume8
dc.description.issue1
dc.description.page16016
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