ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease

Shi, Yuang; Zu, Chen; Mei, Hong; Zhou, Luping; Wang, Lei; Wu, Xi; Zhou, Jiliu; Zhang, Daoqiang; Wang, Yan

doi:10.1016/j.patcog.2022.108566

Cited by 61 publications

(14 citation statements)

References 59 publications

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“…(3) t-test, multi-kernel with t-test; (4) Lasso, multi-kernel with Lasso; (5) Multi-task multi-modality feature selection (M3L) [28], multi-kernel with multi-task; (6) Manifold regularized multi-task multi-modality feature selection (M2TFS) [14], multi-kernel with manifold regularization and multitask; (7) Hypergraph based multi-task multi-modality feature selection (HMTFS) [36], multikernel with hypergraph-based regularization and multi-task; (8) Discriminative multi-task multimodality feature selection (DMTFS) [37]: multi-kernel with discriminative regularization term and multi-task; (9) Clustered multi-task multi-modality feature selection (CMTFS) [38]: multi-kernel with a new spectral norm and multi-task; (10) Adaptive-similarity-based multi-modality feature selection (ASMFS) [39]: multi-kernel with a similarity matrix learned from different modalities and multi-task. The regularization parameters of Lasso, M3L, M2TFS, HMTFS, DMTFS, CMTFS and ASMFS were selected from [1e -5 , 1e -4 , 1e -3 , 1e -2 , 1e -1 ], the parameter for discriminative regularization term in DMTFS and the number of neighbors in ASMFS were chosen from [0, 0.1, 0.2, 0.3, 0.4] and Each domain was alternatively used as the target domain, while the remaining domains were regarded as source domains.…”

Section: Comparison With 10 Multi-modality Feature Selection Methodsmentioning

confidence: 99%

Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders

Ji,

Silva,

Adali

et al. 2024

Preprint

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Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allows us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2%±2.22% and 84.8%±2.68% for the diagnosis of SZ and ASD, respectively. We also compared with 6 domain adaptation (DA), 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA + LOSMFS effectively integrates multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.

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Section: Comparison With 10 Multi-modality Feature Selection Methodsmentioning

confidence: 99%

Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders

Ji,

Silva,

Adali

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unfortunately, the curse of dimensionality limits current methods; too few subjects are available for training compared with large features. Furthermore, feature vectors of high dimension generally contain redundant or irrelevant information, which can lead to overfitting and reduced generalizability of the algorithm (Shi et al 2022 ). The dataset is an important factor directly influencing machine learning performance(Turkoglu et al 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid

Sharshir

Elhelow

Kabeel

et al. 2022

Environ Sci Pollut Res

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This research work intends to enhance the stepped double-slope solar still performance through an experimental assessment of combining linen wicks and cobalt oxide nanoparticles to the stepped double-slope solar still to improve the water evaporation and water production. The results illustrated that the cotton wicks and cobalt oxide (Co3O4) nanofluid with 1wt% increased the hourly freshwater output (HP) and instantaneous thermal efficiency (ITE). On the other hand, this study compares four machine learning methods to create a prediction model of tubular solar still performance. The methods developed and compared are support vector regressor (SVR), decision tree regressor, neural network, and deep neural network based on experimental data. This problem is a multi-output prediction problem which is HP and ITE. The prediction performance for the SVR was the lowest, with 70 (ml/m2 h) mean absolute error (MAE) for HP and 4.5% for ITE. Decision tree regressor has a better prediction for HP with 33 (ml/m2 h) MAE and almost the same MAE for ITE. Neural network has a better prediction for HP with 28 (ml/m2 h) MAE and a bit worse prediction for ITE with 5.7%. The best model used the deep neural network with 1.94 (ml/m2 h) MAE for HP and 0.67% MAE for ITE.

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“…Wang et al 18 used a locality adaptive strategy for multi-modality fusion to generate high-quality PET images. Shi et al 19 proposed a method called Adaptive-Similarity-based Multi-modality Feature Selection (ASMFS) which improves the performance of multimodality feature selection. Recent years of multi-modal end-to-end AD recognition approaches [20][21][22][23][24] have shown that the complement of images from different device sources of Alzheimer's patients can improve the possibility of disease recognition performance.…”

Section: Multi-modality Alzheimer's Recognitionmentioning

confidence: 99%

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

Miao,

Xu,

et al. 2023

Int J Imaging Syst Tech

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Alzheimer's disease (AD) is a severe neurodegenerative disease that can cause dementia symptoms. Currently, most research methods for diagnosing AD rely on fusing neuroimaging data of different modalities to exploit their heterogeneity and complementarity. However, effectively using such multi‐modal information to construct fusion methods remains a challenging problem. To address this issue, we propose a multi‐modal multi‐scale transformer fusion network (MMTFN) for computer‐aided diagnosis of AD. Our network comprises 3D multi‐scale residual block (3DMRB) layers and the Transformer network that jointly learns potential representations of multi‐modal data. The 3DMRB with multi‐scale aggregation efficiently extracts local abnormal information related to AD in the brain. We conducted five experiments to validate our model using MRI and PET images of 720 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed network outperformed existing models, achieving a final classification accuracy of 94.61% for AD and Normal Control.

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ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease

Cited by 61 publications

References 59 publications

Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders

Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders

Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

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