Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification

Fang, Yuqi; Wang, Mingliang; Potter, Guy G.; Liu, Mingxia

doi:10.1016/j.media.2022.102707

Cited by 29 publications

(9 citation statements)

References 143 publications

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“…Transfer Learning (TL), by facilitating the effective knowledge transfer from labeled to unlabeled data, stands at the forefront of innovating medical image classification tasks, particularly in histopathology where the scarcity of labeled data is a prominent issue. Recent studies have explored various transfer learning approaches to leverage unlabeled data and address the limitations of acquiring labeled target data [10][11][12]. For instance, Shi et al [10] proposed a semi-supervised deep transfer learning framework for benign-malignant pulmonary nodule diagnosis by adopting a pre-trained classification network and leveraging available dataset in the network semantic representation space; Feng et al [11] designed a contrastive domain adaptation with consistency match approach for saving labeled chest X-ray in training pneumonia diagnosis models; Fang et al [12] explored a discrepancybased unsupervised cross-domain fMRI adaptation framework for cross-site major depressive disorder identification, by involving attention-guided graph convolution among labeled source and unlabeled target samples.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have explored various transfer learning approaches to leverage unlabeled data and address the limitations of acquiring labeled target data [10][11][12]. For instance, Shi et al [10] proposed a semi-supervised deep transfer learning framework for benign-malignant pulmonary nodule diagnosis by adopting a pre-trained classification network and leveraging available dataset in the network semantic representation space; Feng et al [11] designed a contrastive domain adaptation with consistency match approach for saving labeled chest X-ray in training pneumonia diagnosis models; Fang et al [12] explored a discrepancybased unsupervised cross-domain fMRI adaptation framework for cross-site major depressive disorder identification, by involving attention-guided graph convolution among labeled source and unlabeled target samples. These examples illustrate the innovative potential of transfer learning to bridge the gap between the available data and the analytical capabilities required for precise histopathological diagnosis, highlighting its transformative impact on the field.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty-Guided Asymmetric Consistency Domain Adaptation for Histopathological Image Classification

Yu,

Pei

2024

Applied Sciences

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Deep learning has achieved remarkable progress in medical image analysis, but its effectiveness heavily relies on large-scale and well-annotated datasets. However, assembling a large-scale dataset of annotated histopathological images is challenging due to their unique characteristics, including various image sizes, multiple cancer types, and staining variations. Moreover, strict data privacy in medicine severely restricts data sharing and poses significant challenges in acquiring large-scale and well-annotated histopathological images. To tackle these constraints, Transfer Learning (TL) provides a promising solution by exploiting knowledge from another domain. This study proposes the Uncertainty-guided asymmetric Consistency Domain Adaptation (UCDA), which does not require accessing the source data and is composed of two essential components, e.g., Uncertainty-guided Source-free Transfer Learning (USTL) and Asymmetric Consistency Learning (ACL). In detail, USTL facilitates a secure mapping of the source domain model’s feature space onto the target domain, eliminating the dependency on source domain data to protect data privacy. At the same time, the ACL module measures the symmetry and asymmetry between the source and target domains, bridging the information gap and preserving inter-domain differences among medical images. We comprehensively evaluate the effectiveness of UCDA on three widely recognized and publicly available datasets, namely NCTCRC-HE-100K, PCam, and LC25000. Impressively, our proposed method achieves remarkable performance on accuracy and F1-scores. Additionally, feature visualizations effectively demonstrate the exceptional generalizability and discriminative power of the learned representations. These compelling results underscore the significant potential of UCDA in driving the advancement of deep learning techniques within the realm of histopathological image analysis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty-Guided Asymmetric Consistency Domain Adaptation for Histopathological Image Classification

Yu,

Pei

2024

Applied Sciences

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“…Under these circumstances, rapidly and accurately detecting depression remains a challenging issue, especially given the limitations of existing diagnostic approaches. This is crucial for alleviating the growing mental health crisis [3][4][5].…”

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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“…A large number of machine/deep learning techniques have been proposed to explore potential fMRI biomarkers for early prediction of brain diseases (Guan & Liu, 2023 ; Khosla et al, 2019 ; Li et al, 2018 ; Takenaka et al, 2019 ; Zhang et al, 2018 ). In particular, deep learning methods can automatically learn diagnosis‐oriented fMRI features and perform disease detection in an end‐to‐end manner (Fang et al, 2023 ; Ktena et al, 2018 ; Li et al, 2021 ; Ramzan et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised contrastive graph learning for resting‐state functional MRI analysis and brain disorder detection

Wang,

Chu,

Wang

et al. 2023

Human Brain Mapping

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Resting‐state functional magnetic resonance imaging (rs‐fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning‐based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time‐consuming and labor‐intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI‐based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine‐tuning to perform downstream disease identification tasks. Specifically, in the pretext model, we first design a bi‐level fMRI augmentation strategy to increase the sample size by augmenting blood‐oxygen‐level‐dependent (BOLD) signals, and then employ two parallel graph convolutional networks for fMRI feature extraction in an unsupervised contrastive learning manner. This pretext model can be optimized on large‐scale fMRI datasets, without requiring labeled training data. This model is further fine‐tuned on to‐be‐analyzed fMRI data for downstream disease detection in a task‐oriented learning manner. We evaluate the proposed method on three rs‐fMRI datasets for cross‐site and cross‐dataset learning tasks. Experimental results suggest that the UCGL outperforms several state‐of‐the‐art approaches in automated diagnosis of three brain diseases (i.e., major depressive disorder, autism spectrum disorder, and Alzheimer's disease) with rs‐fMRI data.

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Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification

Cited by 29 publications

References 143 publications

Uncertainty-Guided Asymmetric Consistency Domain Adaptation for Histopathological Image Classification

Uncertainty-Guided Asymmetric Consistency Domain Adaptation for Histopathological Image Classification

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

Unsupervised contrastive graph learning for resting‐state functional MRI analysis and brain disorder detection

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