Journal
SCIENTIFIC REPORTS
Volume 8, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-018-34300-2
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Funding
- Institute of Bioengineering and Nanotechnology
- NMRC [R-185-000-294-511]
- NUHS Innovation Seed Grant 2017 [R-185-000-343-733]
- MOE ARC Tier 2 [R-185-000-342-112]
- SMART BioSyM and Mechanobiology Institute of Singapore [R-714-001-003-271]
- IFCS Program
- EMULSION Program
- Biomedical Research Council
- Agency for Science, Technology and Research (A*STAR)
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Current 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.
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