Depression Detection Using Atlas from fMRI Images

Mousavian, Marzieh; Chen, Jianhua; Greening, Steven G.

doi:10.1109/icmla51294.2020.00210

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Multi-classifier fusion can be roughly divided into two classes in terms of the type of output generated by each classifier: based on the class labels and based on the prediction probability. The first class is generally based on the majority voting principle (Mousavian et al, 2020), which integrates the class labels by the most frequently appeared result in all voting results. Takruri et al proposed to use a majority voting approach to merge individual predictions with multiple features based on different definitions (Takruri et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder

Zhao,

Ye,

Zhang

et al. 2023

Front. Hum. Neurosci.

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IntroductionAutism Spectrum Disorder (ASD) has a significant impact on the health of patients, and early diagnosis and treatment are essential to improve their quality of life. Machine learning methods, including multi-classifier fusion, have been widely used for disease diagnosis and prediction with remarkable results. However, current multi-classifier fusion methods lack the ability to measure the belief level of different samples and effectively fuse them jointly.MethodsTo address these issues, a multi-classifier fusion classification framework based on belief-value for ASD diagnosis is proposed in this paper. The belief-value measures the belief level of different samples based on distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples), which is more representative than using a single type of information. Then, the complementary relationships between belief-values are captured via a multilayer perceptron (MLP) network for effective fusion of belief-values.ResultsThe experimental results demonstrate that the proposed classification framework achieves better performance than a single classifier and confirm that the fusion method used can effectively fuse complementary relationships to achieve accurate diagnosis.DiscussionFurthermore, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to the diagnosis of other neuropsychiatric disorders.

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Section: Resultsmentioning

confidence: 99%

Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder

Zhao,

Ye,

Zhang

et al. 2023

Front. Hum. Neurosci.

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“…For instance, structural magnetic resonance imaging (sMRI) can measure macro-structural changes in the brain, stemming from normal brain development, aging, and even diseases [6][7][8]. Functional magnetic resonance imaging (fMRI) is commonly utilized to track brain functional activities by recording fluctuations of blood oxygen level-dependent (BOLD) [9]. Diffusion tensor imaging (DTI) can be used to explore micro-structural connections and communication pathways in the brain by depicting the trajectories of white matter fiber bundles [10].…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study

Li,

Guo,

Zhao

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Depression ranks among the most prevalent mood-related psychiatric disorders. Existing clinical diagnostic approaches relying on scale interviews are susceptible to individual and environmental variations. In contrast, the integration of neuroimaging techniques and computer science has provided compelling evidence for the quantitative assessment of major depressive disorder (MDD). However, one of the major challenges in computer-aided diagnosis of MDD is to automatically and effectively mine the complementary cross-modal information from limited datasets. In this study, we proposed a few-shot learning framework that integrates multi-modal MRI data based on contrastive learning. In the upstream task, it is designed to extract knowledge from heterogeneous data. Subsequently, the downstream task is dedicated to transferring the acquired knowledge to the target dataset, where a hierarchical fusion paradigm is also designed to integrate features across inter-and intra-modalities. Lastly, the proposed model was evaluated on a set of multi-modal clinical data, achieving average scores of 73.52% and 73.09% for accuracy and AUC, respectively. Our findings also reveal that the brain regions within the default mode network and cerebellum play a crucial role in the diagnosis, which provides further direction in exploring reproducible biomarkers for MDD diagnosis.

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“…Multi-classi er fusion can be roughly divided into two classes in terms of the type of output generated by each classi er: based on the class labels and based on the prediction probability. The rst class is generally based on the majority voting principle [13] , which integrated the class labels by the most frequently appeared result in all voting results. Markus et al proposes to use majority voting approach to merge individual predictions with the multiple features based on different de nitions [14] .…”

Section: Introductionmentioning

confidence: 99%

Multi-classifier fusion base on belief-value for the diagnosis of neuropsychiatric disorders

Zhao

Lü

et al. 2023

Preprint

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Neuropsychiatric disorders seriously affect the health of patients, and early diagnosis and treatment are crucial to improve the quality of patients’ life. Machine learning and other related methods can be used for disease diagnosis and prediction, among which multi-classifier fusion method has been widely studied due to its significant performance over single classifiers. In this paper, we propose a multi-classifier fusion classification framework based on belief-valuefor the neuropsychiatric disorders diagnosis. Specifically, the belief-value measures the belief level of different samples by considering information from two perspectives, which are distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples). The proposed belief-value is more representative compared to the belief-value which only uses a single type of information. Further, based on the concept of multi-view learning, we performed the calculation of the belief-values under the sample space with different features, and the complementary relationship between different belief-values was captured by a multilayer perceptual (MLP) network. Compared with majority voting and linear fusion methods, the MLP network can better capture the nonlinear relationship between belief-values, which produces better diagnostic results. Experimental results show that the proposed method outperforms single classifier and multi-classifier linear fusion methods for the diagnosis of neuropsychiatric disorders.

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Depression Detection Using Atlas from fMRI Images

Cited by 7 publications

References 29 publications

Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder

Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder

Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study

Multi-classifier fusion base on belief-value for the diagnosis of neuropsychiatric disorders

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