A review of medical image data augmentation techniques for deep learning applications

By P. Chlap, Hang Min, Nym Vandenberg, Jason A. Dowling, L. Holloway, A. Haworth. Journal of Medical Imaging and Radiation Oncology, 2021

882 citations recorded by the source corpus.

Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/5c107f5aa33e3e5d3b3ea9dc17e04a51765b0983

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Citations in the corpus

Data Augmentation techniques in time series domain: a survey and taxonomy (2022, background): “In the latest times, several high-quality data augmentation review papers have been published [12, 13, 14].”
Medical image data augmentation: techniques, comparisons and interpretations (2023, methodology): “…methods applied either; (i) with a specific type of images like natural images (Khosla and Saini 2020), mammography images (Oza et al. 2022), Computed Tomography (CT), and Magnetic Resonance (MR) images (Chlap et al. 2021) acquired with different imaging techniques and have different properties.”
Data Augmentation in Classification and Segmentation: A Survey and New Strategies (2023, background): “For reviews on the deep learning approaches for data augmentation, see e.g., [31,32].”
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, background): “Data augmentation [410], [411] and representation learning could [412] be two promising future research directions to mitigate the data inconsistency problem at the edge.”
Data-centric Artificial Intelligence: A Survey (2023, neutral): “For example, it is often expensive and time-consuming to acquire well-annotated medical data [45].”