Addformer: Alzheimer’s Disease Detection from Structural Mri Using Fusion Transformer

Kushol, Rafsanjany; Masoumzadeh, Abbas; Dong, Huo; Kalra, Sanjay; Yang, Yee‐Hong

doi:10.1109/isbi52829.2022.9761421

Cited by 26 publications

(4 citation statements)

References 11 publications

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“…The study in [29] utilizes the vision transformer architecture to automatically detect Alzheimer's patients from healthy controls. The vision transformer architecture is chosen for its ability to effectively capture global or long-range relationships among image features.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease

Almufareh,

Tehsin,

Humayun

et al. 2023

Healthcare

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Alzheimer’s disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer’s in a timely manner to improve the quality of life of both patients and caregivers. In the recent past, machine learning techniques have showed potential in detecting Alzheimer’s disease by examining neuroimaging data, especially Magnetic Resonance Imaging (MRI). This research proposes an attention-based mechanism that employs the vision transformer approach to detect Alzheimer’s using MRI images. The presented technique applies preprocessing to the MRI images and forwards them to a vision transformer network for classification. This network is trained on the publicly available Kaggle dataset, and it illustrated impressive results with an accuracy of 99.06%, precision of 99.06%, recall of 99.14%, and F1-score of 99.1%. Furthermore, a comparative study is also conducted to evaluate the performance of the proposed method against various state-of-the-art techniques on diverse datasets. The proposed method demonstrated superior performance, outperforming other published methods when applied to the Kaggle dataset.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease

Almufareh,

Tehsin,

Humayun

et al. 2023

Healthcare

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“…The purpose of the low-pass filter is to attenuate or remove highfrequency components from F, allowing only low-frequency information to pass through. [1,2,1]]/16 is used as a low-pass filter, which is convolved with F to obtain a low-pass version of F. The amplitude spectrum of the lowpass image is then computed using the FFT, representing the distribution of frequencies present in the low-pass image. Finally, the square root operation is performed to linearize the amplitude spectrum and make it more suitable for interpretation.…”

Section: Frequency Domain Featuresmentioning

confidence: 99%

“…MRI datasets play a vital role in advancing medical research, not only in aiding in the understanding, diagnosing, and treating of numerous neurological disorders but also in training deep learning models. Radiologists and neurologists use MRI scans to identify abnormalities, such as tumours, lesions, atrophy, or other anatomical changes, that may be signs of disorders like Alzheimer’s, multiple sclerosis, epilepsy, and brain tumours [ 1 , 2 , 3 ]. In recent years, the availability of large-scale multi-center datasets has significantly advanced medical imaging research, which opens the avenues for developing powerful machine learning (ML) algorithms and data-driven methodologies.…”

Section: Introductionmentioning

confidence: 99%

DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets

Kushol,

Wilman,

Kalra

et al. 2023

Diagnostics

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In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets.

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“…This enables ViT to effectively capture the dependency relationships between different brain regions in the image. Kushol et al [ 7 ] improved ViT to learn the spatial and frequency-domain features of sMRI images. However, the transformer has limitations in terms of the length of the input sequence and selects only some 2D coronal slices as the input, which may miss critical information.…”

mentioning

confidence: 99%

Computer-aided diagnosis of Alzheimer’s disease based on structural magnetic resonance imaging

Huang,

Shan,

Yan

et al. 2024

Chinese Medical Journal

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Addformer: Alzheimer’s Disease Detection from Structural Mri Using Fusion Transformer

Cited by 26 publications

References 11 publications

Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease

Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease

DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets

Computer-aided diagnosis of Alzheimer’s disease based on structural magnetic resonance imaging

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