GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images

Elazab, Ahmed; Wang, Changmiao; Gardezi, Syed Jamal Safdar; Bai, Hao; Hu, Qingmao; Wang, Tianfu; Chang, Chunqi; Lei, Baiying

doi:10.1016/j.neunet.2020.09.004

Cited by 66 publications

(50 citation statements)

References 48 publications

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“…If an image has one or more contours associated with it, the same transformation is applied to the contours. Geometric transformations are so common that they were utilised by 92 of the 93 basic augmentation studies 15–106 …”

Section: Methodsmentioning

confidence: 99%

“…Patches are generated from the under‐represented class to even the balance. 30 articles made use of cropping 15–17,19,23,32,33,35,41,43,48,50,58,59,66,68,69,71,74,78,80,82,84,85,90,97,98,102,104,105 …”

Section: Methodsmentioning

confidence: 99%

“…30 articles made use of cropping. [15][16][17]19,23,32,33,35,41,43,48,50,58,59,66,68,69,71,74,78,80,82,84,85,90,97,98,102,104,105…”

Section: Croppingmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

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Summary 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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…In order to simulate the non-deterministic and unknown parameters involved in the evolution process, a Gaussian noise vector is added to the DEP model as an auxiliary input, which forces the DEP model to simulate a wider range of prediction results. Elazab et al [96] proposed a stacked 3D GAN for predicting glioma growth. The generator is designed based on a modified 3D U-Net architecture with skip connections to combine hierarchical features.…”

Section: Predictionmentioning

confidence: 99%

When medical images meet generative adversarial network: recent development and research opportunities

Jiang

Rodríguez-Andina

et al. 2021

Discov Artif Intell

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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.

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“…Generative learning models have been recently gained considerable attention due to their surprising performance in producing highly realistic signals of various types [ 1 , 2 , 3 , 4 ]. They have been successfully employed in a wide variety of applications, such as image-to-image translation [ 5 ], image fusion [ 6 ], face de-identification [ 7 ], natural language generation [ 8 ], data augmentation on ancient handwritten characters [ 9 ], MRI super-resolution [ 10 ], brain tumor growth prediction [ 11 ], generative modeling of structured-data [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Information-Theoretic Perspective on Proper Quaternion Variational Autoencoders

Grassucci

Comminiello

Uncini

2021

Entropy

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Variational autoencoders are deep generative models that have recently received a great deal of attention due to their ability to model the latent distribution of any kind of input such as images and audio signals, among others. A novel variational autoncoder in the quaternion domain H, namely the QVAE, has been recently proposed, leveraging the augmented second order statics of H-proper signals. In this paper, we analyze the QVAE under an information-theoretic perspective, studying the ability of the H-proper model to approximate improper distributions as well as the built-in H-proper ones and the loss of entropy due to the improperness of the input signal. We conduct experiments on a substantial set of quaternion signals, for each of which the QVAE shows the ability of modelling the input distribution, while learning the improperness and increasing the entropy of the latent space. The proposed analysis will prove that proper QVAEs can be employed with a good approximation even when the quaternion input data are improper.

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GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images

Cited by 66 publications

References 48 publications

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

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

When medical images meet generative adversarial network: recent development and research opportunities

An Information-Theoretic Perspective on Proper Quaternion Variational Autoencoders

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