Learning to synthesise the ageing brain without longitudinal data

Xia, Tian; Chartsias, Agisilaos; Wang, Chengjia; Tsaftaris, Sotirios A.; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1016/j.media.2021.102169

Cited by 38 publications

(49 citation statements)

References 63 publications

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“…In this paper, x is a brain image, y is the AD diagnosis of x, and v represents the target age a and AD diagnosis on which the generator G is conditioned. We use the brain ageing generation model proposed in [28] as G, and a VGG-based [25] AD classification model as C. Note that we only change the target age a in this paper, thus we write the generative process as x = G(x, a) for simplicity.…”

Section: Notations and Problem Overviewmentioning

confidence: 99%

“…The brain ageing generative model used in this paper is adopted from a recent work [28], which takes a brain image and a target age as inputs and outputs an aged brain image 3 . The original model used ordinal encoding to encode the conditional age and AD diagnosis, where the encoded vectors are discrete in nature, which hinders gradient backpropagation to update these vectors.…”

Section: Fourier Encoding For Conditional Factorsmentioning

confidence: 99%

“…2. We extend a recent brain ageing synthesis model [28] by involving Fourier encoding to enable gradient backpropagation to update conditional factor and demonstrate the effectiveness of our approach on the task of AD classification. 3.…”

Section: Introductionmentioning

confidence: 98%

“…3. We extend [28] for brain rejuvenation synthesis and demonstrate that the proposed approach has the potential to alleviate spurious correlations.…”

Section: Introductionmentioning

confidence: 99%

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Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Xia¹,

Sánchez²,

Chen³

et al. 2022

Preprint

Self Cite

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Data augmentation has been widely used in deep learning to reduce over-fitting and improve the robustness of models. However, traditional data augmentation techniques, e.g., rotation, cropping, flipping, etc., do not consider semantic transformations, e.g., changing the age of a brain image. Previous works tried to achieve semantic augmentation by generating counterfactuals, but they focused on how to train deep generative models and randomly created counterfactuals with the generative models without considering which counterfactuals are most effective for improving downstream training. Different from these approaches, in this work, we propose a novel adversarial counterfactual augmentation scheme that aims to find the most effective counterfactuals to improve downstream tasks with a pre-trained generative model. Specifically, we construct an adversarial game where we update the input conditional factor of the generator and the downstream classifier with gradient backpropagation alternatively and iteratively. The key idea is to find conditional factors that can result in hard counterfactuals for the classifier. This can be viewed as finding the 'weakness' of the classifier and purposely forcing it to overcome its weakness via the generative model. To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as the downstream task based on a pre-trained brain ageing synthesis model. We show the proposed approach improves test accuracy and can alleviate spurious correlations. Code will be released upon acceptance.

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Section: Notations and Problem Overviewmentioning

confidence: 99%

Section: Fourier Encoding For Conditional Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

“…3. We extend [28] for brain rejuvenation synthesis and demonstrate that the proposed approach has the potential to alleviate spurious correlations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Xia¹,

Sánchez²,

Chen³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, GANs have also been applied to longitudinal MR image prediction. For example, Xia et al (2019) proposed a conditional GAN that conditioned on age and health state (status of Alzheimer's Disease) to predict brain aging trajectories. In (Bowles et al, 2018;Ravi et al, 2019), a GAN is used to predict the Alzheimer's related brain degeneration from existing MR images, where biological constraints associated with disease progression are integrated into the framework.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

Peng

Lin

et al. 2021

Front. Neurosci.

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The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

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A Deep Generative Model of Neonatal Cortical Surface Development

Fawaz

Williams

Edwards

2022

Medical Image Understanding and Analysis

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The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional filters are inappropriate on non-flat topologies. To close this gap, we implement a surfacebased CycleGAN using mixture model CNNs (MoNet) to translate sphericalised neonatal cortical surface features (curvature and T1w/T2w cortical myelin) between different stages of cortical maturity. Results show our method is able to reliably predict changes in individual patterns of cortical organisation at later stages of gestation, validated by comparison to longitudinal data; and translate appearance between preterm and term gestation (> 37 weeks gestation), validated through comparison with a trained term/preterm classifier. Simulated differences in cortical maturation are consistent with observations in the literature.

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Learning to synthesise the ageing brain without longitudinal data

Cited by 38 publications

References 63 publications

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

A Deep Generative Model of Neonatal Cortical Surface Development

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