Author name cluster

Xiaochen Zhang

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5 papers

2 author rows

JBHI Journal 2025 Journal Article

FROG: A Fine-Grained Spatiotemporal Graph Neural Network With Self-Supervised Guidance for Early Diagnosis of Alzheimer's Disease

Shuoyan Zhang
Qingmin Wang
Min Wei
Jiayi Zhong
Ying Zhang
Ziyan Song
Chenyang Li
Xiaochen Zhang

Functional magnetic resonance imaging (fMRI) has demonstrated significant potential in the early diagnosis and study of pathological mechanisms of Alzheimer's disease (AD). To fit subtle cross-spatiotemporal interactions and learn pathological features from fMRI, we propose a fine-grained spatiotemporal graph neural network with self-supervised learning (SSL) for diagnosis and biomarker extraction of early AD. First, considering the spatiotemporal interaction of the brain, we design two masks that leverage the spatial correlation and temporal repeatability of fMRI. Afterwards, temporal gated inception convolution and graph scalable inception convolution are proposed for the spatiotemporal autoencoder to enhance subtle cross-spatiotemporal variation and learn noise-suppressed signals. Furthermore, a spatiotemporal scalable cosine error with high selectivity for signal reconstruction is designed in SSL to guide the autoencoder to fit the fine-grained pathological features in an unsupervised manner. A total of 5, 687 samples from four cross-population cohorts are involved. The accuracy of our model was 5. 1% higher than the state-of-the-art models, which included four AD diagnostic models, four SSL strategies, and three multivariate time series models. The neuroimaging biomarkers were precisely localized to the abnormal brain regions, and correlated significantly with the cognitive scale and biomarkers (P $< $ 0. 001). Moreover, the AD progression was reflected through the mask reconstruction error of our SSL strategy. The results demonstrate that our model can effectively capture spatiotemporal and pathological features, and providing a novel and relevant framework for the early diagnosis of AD based on fMRI.

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EAAI Journal 2025 Journal Article

Self-supervised deep contrastive and auto-regressive domain adaptation for time-series based on channel recalibration

Guangju Yang
Tian-jian Luo
Xiaochen Zhang

Time-series based unsupervised domain adaptation (UDA) techniques have been widely adopted to the applications of intelligent systems, such as sleep staging, fault diagnosis, and human activity recognition. However, recently methods have overlooked the importance of temporal feature representations and the distribution discrepancies across domains, which deteriorated UDA performance. To address these challenges, we proposed a novel Self-supervised Deep Contrastive and Auto-regressive Domain Adaptation (SDCADA) model for cross-domain time-series classification. Specifically, the cross-domain mixup preprocessing strategy is applied to reduce sample-level distribution discrepancy, then we proposed to introduce the channel recalibration module for adaptively selecting discriminative representations. Afterwards, the auto-regressive discriminator and teacher model are proposed to reduce the distribution discrepancies of feature representations. Finally, a total of six losses, including contrastive and adversarial learning, are weighted and jointly optimized to train the SDCADA model. The proposed SDCADA model has been systematically experimented on four cross-domain time-series benchmarked datasets, and its classification performance surpasses several recently proposed state-of-the-art models. Moreover, it effectively captures discriminative and comprehensive cross-domain time-series feature representations with parameter insensitivity.

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JBHI Journal 2024 Journal Article

S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test

Zeshen Tang
Shiyu Tang
Haoran Wang
Renren Li
Xiaochen Zhang
Wei Zhang
Xiao Yuan
Yaning Zang

Background: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. Methodology: In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. Results: The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. Conclusions: In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff.

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ICLR Conference 2024 Conference Paper

Self-supervised Representation Learning from Random Data Projectors

Yi Sui 0001
Tongzi Wu
Jesse C. Cresswell
Ga Wu
George Stein
Xiao Shi Huang
Xiaochen Zhang
Maksims Volkovs

Self-supervised representation learning (SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. While augmentation-based SSRL algorithms push the boundaries of performance in computer vision and natural language processing, they are often not directly applicable to other data modalities, and can conflict with application-specific data augmentation constraints. This paper presents an SSRL approach that can be applied to any data modality and network architecture because it does not rely on augmentations or masking. Specifically, we show that high-quality data representations can be learned by reconstructing random data projections. We evaluate the proposed approach on a wide range of representation learning tasks that span diverse modalities and real-world applications. We show that it outperforms multiple state-of-the-art SSRL baselines. Due to its wide applicability and strong empirical results, we argue that learning from randomness is a fruitful research direction worthy of attention and further study.

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ICRA Conference 2011 Conference Paper

Theseus gradient guide: An indoor transmitter searching approach using received signal strength

Xiaochen Zhang
Yi Sun 0005
Jizhong Xiao
Flavio Cabrera-Mora

The searching for a location-unknown radio transmitter is a challenging task for autonomous robot. We propose an adaptive searching algorithm named theseus gradient guide (TGG) which is designed for solving the searching problem in indoor environments using received signal strength (RSS). While the RSS gradient serves as the main guide, the robot prefers to move to the places which have never been traveled. Thus the robot will not get stuck in the local maxima. Moreover, unlike the commonly used random kick strategy the TGG drives the robot escaping the local maxima with low cost in terms of travel distance. Meanwhile, TGG is not sensitive to motion errors. Simulation results show that the searches using TGG cost much less compared with those using other gradient based methods in our testing indoor environment. Guided by TGG, the robot can successfully reach the location-unknown radio transmitter with a ratio over 97% when the standard deviation of motion error is up to 20% of the step length.

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