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

🤖 Plain-English Summary

Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning-based algorithms. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques.

🔑 Key Findings

• 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.

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