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Geostatistical Simulation of Medical Images for Data Augmentation in Deep Learning
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. (Pattern Recognition)ORCID iD: 0000-0002-4255-5130
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 68752-68763Article in journal (Refereed) Published
Abstract [en]

Data augmentation, which is the process for creating alternative copies of each sample in a small training data set, is important for extracting deep-learning features for medical image classification problems. However, data augmentation has not been well explored and existing methods are heuristic. In this paper, a geostatistical simulation of images is introduced as a data augmentation approach for extracting deep-learning features from medical images that are characterized with texture. The stochastic simulation procedure is to generate realizations of an image by modeling its spatial variability through the generation of multiple equiprobable stochastic realizations. The approach employed for the geostatistical simulation is based on the concepts of regionalized variables and kriging formulation to create multiple textural variations of medical images. Experimental results on classifying two medical-image data sets show that the use of geostatistical simulation for extracting deep-learning features with several popular pre-trained deep-learning models (AlexNet, ResNet-50, and GoogLeNet/Inception) provided better accuracy rates and more balanced results in terms of sensitivity and specificity than either with or without the implementation of conventional data augmentation. The proposed approach can be applied to other pre-trained networks as well as those without data pre-training for effective deep-feature extraction from medical images. The proposed application of the theory of geostatistics for medical image data augmentation in deep learning is original. The novelty of this approach can also be applied to many other types of data that are inherently textural. Geostatistical simulation opens a new door to the development of state-of-the-art artificial intelligence in feature extraction of medical images.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 7, p. 68752-68763
Keywords [en]
Medical image features, sequential Gaussian simulation, geostatistics, deep learning, convolutional neural networks, classification.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-157171DOI: 10.1109/ACCESS.2019.2919678ISI: 000471349000001OAI: oai:DiVA.org:liu-157171DiVA, id: diva2:1319450
Available from: 2019-06-01 Created: 2019-06-01 Last updated: 2019-10-29Bibliographically approved

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Pham, Tuan
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