Fader networks for domain adaptation on fMRI: ABIDE-II study

Pominova, Marina; Kondrateva, Ekaterina; Sharaev, Maxim; Bernstein, Alexander; Burnaev, Evegeny

doi:10.1117/12.2587348

Cited by 9 publications

(3 citation statements)

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“…SVM and K-nearest neighbors (KNN) were used for ASD classification, and KNN achieved the highest classification accuracy. Pominova et al [117] utilized extracted features of FC matrices and full-size MRI series with a 3D convolutional autoencoder method. To classify ASD and HC subjects, the SVM classifier was used.…”

Section: The Role Of Ai For Asd Diagnosis Using Fmrimentioning

confidence: 99%

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Helmy,

Elnakib,

ElNakieb

et al. 2023

Biomedicines

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Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging—more specifically, magnetic resonance imaging (MRI)—has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

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Section: The Role Of Ai For Asd Diagnosis Using Fmrimentioning

confidence: 99%

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Helmy,

Elnakib,

ElNakieb

et al. 2023

Biomedicines

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“…The Fader network is a type of domain adaptation technique. The most significant advantage of the Fader network 13,14 is that it can simultaneously harmonize MRI neuroimaging data at multiple sites. Still, as the number of sites increases, the model's training becomes unstable.…”

Section: Introductionmentioning

confidence: 99%

Auto-fader networks for harmonization on grey matter images

Yan,

Hao,

Zhou

et al. 2023

Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023)

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“…Wang et al ( 2019 ) explored ways to use low-Rank DA to reduce existing biases on multi-site fMRI datasets. Recent approaches include transport-based joint distribution alignment (Zhang et al, 2020 ), federated learning (Li et al, 2020 ), and conditional autoencoder (Fader Networks) (Pominova et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study

Panda

Kalmady

Greiner

2022

Front. Neuroinform.

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The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis. Such DL-based methods are generally data-intensive and require a large number of training instances, which might be infeasible to acquire from a single acquisition site, especially for data, such as fMRI scans, due to the time and costs that they demand. We can attempt to address this issue by combining fMRI data from various sites, thereby creating a bigger heterogeneous dataset. Unfortunately, the inherent differences in the combined data, known as batch effects, often hamper learning a model. To mitigate this issue, techniques such as multi-source domain adaptation [Multi-source Domain Adversarial Networks (MSDA)] aim at learning an effective classification function that uses (learned) domain-invariant latent features. This article analyzes and compares the performance of various popular MSDA methods [MDAN, Domain AggRegation Networks (DARN), Multi-Domain Matching Networks (MDMN), and Moment Matching for MSDA (M3SDA)] at predicting different labels (illness, age, and sex) of images from two public rs-fMRI datasets: ABIDE 1and ADHD-200. It also evaluates the impact of various conditions such as class imbalance, the number of sites along with a comparison of the degree of adaptation of each of the methods, thereby presenting the effectiveness of MSDA models in neuroimaging-based applications.

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Fader networks for domain adaptation on fMRI: ABIDE-II study

Cited by 9 publications

References 0 publications

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Auto-fader networks for harmonization on grey matter images

Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study

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