Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis

Jeon, Eunjin; Kang, Eunsong; Lee, Jae-In; Kam, Tae Eui; Suk, Heung-Il

doi:10.1007/978-3-030-59728-3_39

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…[8] analyzed the gray matter images of schizophrenia MRI using VBM and ROI and classified schizophrenia patients using SVM. [9] directly input the time sequence features of ROI regions into the network to learn spatial and temporal features using convolution and attention mechanisms. [10] uses an overlapping sliding window to divide rs-fMRI time series into segments, then builds a longitudinally ordered functional connectivity network.…”

Section: Related Workmentioning

confidence: 99%

Beijing Language and Culture University, China

Wang¹

2022

The Experience of Examining the PhD

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In the study of network synchronization, an outstanding question of both theoretical and practical significance is how to allocate a given set of heterogenous oscillators on a complex network in order for improving the synchronization performance. Whereas methods have been proposed to address this question in literature, the methods are based on accurate models describing the system dynamics, which, however, are normally unavailable in realistic situations. Here we show that this question can be addressed by the model-free technique of feed-forward neural network (FNN) in machine learning. Specifically, we measure the synchronization performance of a number of allocation schemes and use the measured data to train a machine. It is found that the trained machine is able to not only infer the synchronization performance of any new allocation scheme, but also find from a huge amount of candidates the optimal allocation scheme for synchronization.

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Section: Related Workmentioning

confidence: 99%

Beijing Language and Culture University, China

Wang¹

2022

The Experience of Examining the PhD

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“…In recent decades, DL has been utilized to diagnose brain diseases using rs-fMRI [9], [11], [18], [19], [21]- [23], [35]. In [9] and [11], diagnosis tasks were performed using the raw time signals of rs-fMRI.…”

Section: Related Work a Dl-based Brain Disease Diagnosis Methods In R...mentioning

confidence: 99%

“…Rs-fMRI is a non-invasive technique that identifies spatio-temporal scales of regional brain activation by measuring blood-oxygen-leveldependent (BOLD) signals [8]. Most existing rs-fMRI studies utilize raw time signals [9]- [11] or low-order brain functional connectivity (FC) [12], [13]. FC is typically constructed based on the temporal correlation between spatially remote brain regions-regions of interest (ROIs)in a statistical manner [14], [15].…”

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confidence: 99%

EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning

Jung,

Jeon,

Kang

et al. 2024

IEEE Trans. Med. Imaging

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Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.e., different symptoms or varying stages of severity) among patients with ASD, and the non-explainability of the decision process. To cover these limitations, we propose a novel explainability-guided region of interest (ROI) selection (EAG-RS) framework that identifies non-linear high-order functional associations among brain regions by leveraging an explainable artificial intelligence technique and selects class-discriminative regions for brain disease identification. The proposed framework includes three steps: (i) inter-regional relation learning to estimate nonlinear relations through random seed-based network masking, (ii) explainable connection-wise relevance score estimation to explore high-order relations between functional connections, and (iii) non-linear high-order FC-based diagnosis-informative ROI selection and classifier learning to identify ASD. We validated the effectiveness of our proposed method by conducting experiments using the Autism Brain Imaging Database Exchange (ABIDE) dataset, demonstrating that the proposed method outperforms other comparative methods in terms of various evaluation metrics. Furthermore, we qualitatively analyzed the selected ROIs and identified ASD subtypes linked to previous neuroscientific studies.

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“…Self-supervised learning has fueled recent advances in image recognition (Oord et al, 2018;Hjelm et al, 2018;Bachman et al, 2019;Tian et al, 2019;Grill et al, 2020;Chen & He, 2020) and spurred great interest and high expectations in neuroimaging (Fedorov et al, 2019;Mahmood et al, 2020;Jeon et al, 2020;Taleb et al, 2020;Fedorov et al, 2020b). The expectations are generally high even outside neuroimaging, so much so that in Yann LeCun's metaphor of learning as a cake (LeCun, 2019), self-supervised learning makes the tastiest part of the cake: the filling.…”

Section: Introductionmentioning

confidence: 99%

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data

Fedorov¹,

Geenjaar²,

Wu³

et al. 2021

Preprint

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Self-supervised learning has enabled significant improvements on natural image benchmarks. However, there is less work in the medical imaging domain in this area. The optimal models have not yet been determined among the various options. Moreover, little work has evaluated the current applicability limits of novel self-supervised methods. In this paper, we evaluate a range of current contrastive self-supervised methods on out-of-distribution generalization in order to evaluate their applicability to medical imaging. We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout. We also show that this behavior can be countered with extensive augmentation. Our results highlight the need for out-of-distribution generalization standards and benchmarks to adopt the self-supervised methods in the medical imaging community.

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Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis

Cited by 15 publications

References 14 publications

Beijing Language and Culture University, China

Beijing Language and Culture University, China

EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data

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