Alzheimer’s disease classification accuracy is Improved by MRI harmonization based on attention-guided generative adversarial networks

Sinha, Sujata; Thomopoulos, Sophia I.; Lam, Pradeep; Muir, Alexandra M.; Thompson, Paul M.

doi:10.1117/12.2606155

Cited by 14 publications

(12 citation statements)

References 25 publications

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“…For instance, Liu et al [4] used style transfer methods to match brain MRI scans to a reference dataset. Sinha et al [5] used attention-guided GANs for harmonization and demonstrated improvements in Alzheimer's disease classification with harmonized data. Dinsdale et al [2] developed a deep learning-based model to remove dataset bias while improving performance on a downstream task of brain age prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Two broad categories of domain adaptation methods have emerged: (1) adversarial methods that map source data into a site-invariant latent space [1,2,7], where features are optimized for the main task (e.g., disease detection) but also adapted to defeat an adversary that tries to predict which site the data came from; and (2) synthetic methods that also synthesize a new image to appear as if it came from another scanner, often using neural style transfer methods [4,5]. Such reconstruction methods can also be extended to cross-modal data synthesis (e.g., simulating PET or CT scans from MRI) or for image enhancement with super-resolution [3].…”

Section: Introductionmentioning

confidence: 99%

“…Dewey et al [6] presented DeepHarmony, a UNET-based architecture that requires a paired dataset for training. Many works limit the harmonization training to 2D slices rather than the whole 3D volume [2, 5, 6]. Zuo et al [7] developed CALAMITI, which uses information bottleneck theory to learn a disentangled latent space that contains both anatomical and contrast information, enabling controllable harmonization in a parametrized protocol space.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Harmonization of Brain Mri Using 3d Cycle Gans and Its Effect on Brain Age Prediction

Komandur

Gupta

Chattopadhyay

et al. 2022

Preprint

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Deep learning methods trained on brain MRI data from one scanner or imaging protocol can fail catastrophically when tested on data from other sites or protocols - a problem known as domain shift. To address this, here we propose a domain adaptation method that trains a 3D CycleGAN (cycle-consistent generative adversarial network) to harmonize brain MRI data from 5 diverse sources (ADNI, WHIMS, OASIS, AIBL, and UK Biobank; total N=4,941 MRIs, age range: 46-96 years). The approach uses 2 generators and 2 discriminators to generate an image harmonized to a specific target dataset given an image from the source domain distribution and vice versa. We train the CycleGAN to jointly optimize an adversarial loss and cyclic consistency. We use a patch-based discriminator and impose identity loss to further regularize model training. To test the benefit of the harmonization, we show that brain age estimation - a common benchmarking task - is more accurate in GAN-harmonized versus raw data. t-SNE maps show the improved distributional overlap of the harmonized data in the latent space.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised Harmonization of Brain Mri Using 3d Cycle Gans and Its Effect on Brain Age Prediction

Komandur

Gupta

Chattopadhyay

et al. 2022

Preprint

Self Cite

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“…Generative adversarial networks (GANs) can increase feature complexity by generating features (Goodfellow et al, 2020 ; Park et al, 2021 ; Yi et al, 2019 ). Previous deep learning studies that adopted the GANs model have found that the GANs model can improve the classification accuracy of some diseases, including AD (Sinha et al, 2021 ; Zhou et al, 2021 ) and major depressive disorder (Zhao, Chen, et al, 2020 ). Hence, the application of deep learning using GANs and DNN may provide a new measure to examine whether frequency‐specific FC alterations could be used as neuromarkers for ASD.…”

Section: Introductionmentioning

confidence: 99%

Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

Cao

et al. 2022

Human Brain Mapping

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Previous studies have explored resting‐state functional connectivity (rs‐FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency‐specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age‐matched and sex‐matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed‐based FC analyses in the conventional, slow‐5, and slow‐4 frequency bands at each site. Image‐based meta‐analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta‐analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow‐5 frequency band instead of the conventional and slow‐4 frequency bands. The deep learning results showed that, compared with the conventional and slow‐4 frequency bands, the slow‐5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.

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“…The stability of AD classifiers across scanning protocols and datasets has also begun to be examined. Models based on generative adversarial networks (GANs [3,4,5]) show promise in adapting data to work well with pretrained models, compensating for the so-called “domain shift [12]”.…”

Section: Introductionmentioning

confidence: 99%

Predicting Dementia Severity by Merging Anatomical and Diffusion MRI with Deep 3D Convolutional Neural Networks

Chattopadhyay

Singh

Joshy

et al. 2022

Preprint

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Machine learning methods have been used for over a decade for staging and subtyping a variety of brain diseases, offering fast and objective methods to classify neurodegenerative diseases such as Alzheimers disease (AD). Deep learning models based on convolutional neural networks (CNNs) have also been used to infer dementia severity and predict future clinical decline. Most CNN-based deep learning models use T1-weighted brain MRI scans to identify predictive features for these tasks. In contrast, we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models of dementia severity. dMRI is sensitive to microstructural brain abnormalities not evident on standard anatomical MRI. By training CNNs on combined anatomical and diffusion MRI, we hypothesize that we could boost performance when predicting widely used clinical assessments of dementia severity, such as individuals scores on the ADAS11, ADAS13, and MMSE (mini-mental state exam) clinical scales. For benchmarking, we evaluate CNNs that use T1-weighted MRI and dMRI to estimate brain age - the task of predicting a persons chronological age from their neuroimaging data. To assess which dMRI-derived maps were most beneficial, we computed DWI-derived diffusion tensor imaging (DTI) maps of mean and radial diffusivity (MD/RD), axial diffusivity (AD) and fractional anisotropy (FA) for 1198 elderly subjects (age: 74.35 +/- 7.74 yrs.; 600 F/598 M, with a distribution of 636 CN/421 MCI/141 AD) from the Alzheimers Disease Neuroimaging Initiative (ADNI). We tested both 2D Slice CNN and 3D CNN neural network models for the above predictive tasks. Our results suggest that for at least some deep learning architectures, diffusion-weighted MRI may enhance performance for several AD-relevant deep learning tasks relative to using T1-weighted images alone.

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Alzheimer’s disease classification accuracy is Improved by MRI harmonization based on attention-guided generative adversarial networks

Cited by 14 publications

References 25 publications

Unsupervised Harmonization of Brain Mri Using 3d Cycle Gans and Its Effect on Brain Age Prediction

Unsupervised Harmonization of Brain Mri Using 3d Cycle Gans and Its Effect on Brain Age Prediction

Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

Predicting Dementia Severity by Merging Anatomical and Diffusion MRI with Deep 3D Convolutional Neural Networks

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