Abstract
Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed well in computer vision. Medical image analysis is an important application of deep learning, which is expected to greatly reduce the workload of doctors, contributing to more sustainable health systems. However, most current AI methods for medical image analysis are based on supervised learning, which requires a lot of annotated data. The number of medical images available is usually small and the acquisition of medical image annotations is an expensive process. Generative adversarial network (GAN), an unsupervised method that has become very popular in recent years, can simulate the distribution of real data and reconstruct approximate real data. GAN opens some exciting new ways for medical image generation, expanding the number of medical images available for deep learning methods. Generated data can solve the problem of insufficient data or imbalanced data categories. Adversarial training is another contribution of GAN to medical imaging that has been applied to many tasks, such as classification, segmentation, or detection. This paper investigates the research status of GAN in medical images and analyzes several GAN methods commonly applied in this area. The study addresses GAN application for both medical image synthesis and adversarial learning for other medical image tasks. The open challenges and future research directions are also discussed.
Highlights
Deep learning has dominated the field of computer vision since 2012 [1], taking advantage of the huge improvement in data storage and computing power of modern processing devices
CycleGAN [16] proposes a ring closed network consisting of two generators and two discriminators (Fig. 2c), which performs the conversion between two image domains without the need of paired images
The time series prediction models commonly used in deep learning include recurrent neural network (RNN) and long short-term memory (LSTM), among others, but these models are more suitable for time series signal vectors, whereas medical images have higher dimensions
Summary
Deep learning has dominated the field of computer vision since 2012 [1], taking advantage of the huge improvement in data storage and computing power of modern processing devices. Most advanced methods for computer vision are based on deep learning In this context, medical image analysis is an important research direction. Most current artificial intelligence (AI) methods and applications belong to the category of supervised learning [3], which in this case means medical image data must be labeled. This is very difficult and costly to achieve in practice. If it is applied to medical imaging, it can expand datasets with insufficient amounts of medical image data so that deep learning methods can be used together with the expanded datasets Another very useful feature of GAN for medical image analysis is its adversarial training strategy, which can be applied to image segmentation, detection, or classification.
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