Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification

Wang, Mingliang; Zhang, Daoqiang; Huang, Jiashuang; Shen, Dinggang; Liu, Mingxia

doi:10.1007/978-3-030-00928-1_73

Cited by 20 publications

(8 citation statements)

References 11 publications

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“…Finally, the utilization of multi-site data for disease analysis has recently attracted increased attention (Heinsfeld et al, 2018;Wang et al, 2018Wang et al, , 2019c) since a large number of subjects from multiple imaging sites are beneficial for investigating the pathological changes of disease-affected brains. Previous methods often suffer from inter-site heterogeneity caused by different scanning parameters and subject populations in different imaging sites by assuming that these multi-site data are drawn from the same data distribution.…”

Section: Discussion and Future Directionmentioning

confidence: 99%

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

et al. 2020

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Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.

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Section: Discussion and Future Directionmentioning

confidence: 99%

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

et al. 2020

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“…In the medical field (also see later section), we have [123], [124] (uses Laplacian eigenmap (LE) for interpretability), and [125] (introduces a low-rank representation method for autistic spectrum diagnosis). c) Challenges and future prospects: This section exemplifies the difficulty in integrating mathematics and human intuition.…”

Section: B Interpretability Via Mathematical Structurementioning

confidence: 99%

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa

Guan

2021

IEEE Trans. Neural Netw. Learning Syst.

1,195

547

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Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide "obviously" interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.

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“…Because rank minimization problem is non-convex. Inspired by Wang et al (2018Wang et al ( , 2019, ( 4) is reformulated to min…”

Section: Problem Formulation Of Unified Brain Networkmentioning

confidence: 99%

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Zhu

Yang²,

Xu³

et al. 2021

Front. Neurosci.

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Brain network analysis has been proved to be one of the most effective methods in brain disease diagnosis. In order to construct discriminative brain networks and improve the performance of disease diagnosis, many machine learning–based methods have been proposed. Recent studies show that combining functional and structural brain networks is more effective than using only single modality data. However, in the most of existing multi-modal brain network analysis methods, it is a common strategy that constructs functional and structural network separately, which is difficult to embed complementary information of different modalities of brain network. To address this issue, we propose a unified brain network construction algorithm, which jointly learns both functional and structural data and effectively face the connectivity and node features for improving classification. First, we conduct space alignment and brain network construction under a unified framework, and then build the correlation model among all brain regions with functional data by low-rank representation so that the global brain region correlation can be captured. Simultaneously, the local manifold with structural data is embedded into this model to preserve the local structural information. Second, the PageRank algorithm is adaptively used to evaluate the significance of different brain regions, in which the interaction of multiple brain regions is considered. Finally, a multi-kernel strategy is utilized to solve the data heterogeneity problem and merge the connectivity as well as node information for classification. We apply the proposed method to the diagnosis of epilepsy, and the experimental results show that our method can achieve a promising performance.

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Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification

Cited by 20 publications

References 11 publications

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

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