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Tianyou Yu

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4 papers

2 author rows

JBHI Journal 2026 Journal Article

Efficient Sleep Staging With Bayesian Uncertainty-Guided Active Learning

Tianyou Yu
Rui Huang
Fei Wang
Jun Zhang
Wei Wu
Zhuliang Yu
Yuanqing Li
Jun Xiao

Automated sleep staging is essential for large-scale and home-based sleep monitoring; however, in routine clinical practice, sleep annotation remains largely dependent on experienced experts performing time-consuming and labor-intensive manual scoring. Existing automatic systems often struggle to adapt reliably to new subjects, limiting their clinical adoption and reinforcing the reliance on expert review. This creates a strong demand for adaptive and efficient sleep staging systems that can substantially reduce annotation workload while preserving expert-level accuracy. We propose BayesSleepNet, a novel framework that integrates Bayesian uncertainty quantification with active learning for adaptive sleep staging. BayesSleepNet employs principled Bayesian modeling by placing distributions over network weights and performing Monte Carlo sampling at inference, enabling explicit quantification of model (epistemic) uncertainty. These uncertainty estimates drive a two-stage sample selection strategy that first fine-tunes the model using representative epochs and subsequently prioritizes persistently uncertain samples for expert review. Across four public sleep datasets, BayesSleepNet consistently improves performance—by 7. 60% in accuracy, 8. 27% in macro-F1, and 0. 104 in Cohen's $\kappa$ —while requiring manual annotation of only 20% of data from new subjects. Despite its adaptive learning capability, BayesSleepNet remains computationally lightweight, using substantially fewer parameters than representative high-capacity state-of-the-art models. These results demonstrate the clinical promise of uncertainty-aware active learning as a practical and cost-efficient paradigm for semi-automated sleep staging. Code is available at https://github.com/yuty2009/bayesugal.

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YNIMG Journal 2026 Journal Article

VSSI 2 p -Net: Physics-guided deep unfolding with L 2 p -norm and variation sparsity for EEG source imaging

Luhua Wang
Jun Zhang
Zhenghui Gu
Ke Liu
Wei Wu
Tianyou Yu
Zhuliang Yu
Yuanqing Li

Electroencephalogram (EEG) source imaging (ESI) is highly underdetermined, which poses a long-standing challenge in neuroimaging. Traditional methods typically rely on predefined priors to constrain the solution space; however, the need for manual parameter adjustments often makes it difficult to achieve optimal integration of prior information. Although recent deep learning methods can automatically update parameters in a data-driven manner, their black-box characteristics lead to a lack of interpretability and the need for extensive training sets. To integrate the advantages of these two types of methods, we propose a novel neural network model based on deep unfolding, called variation sparse source imaging network (VSSI 2 p -Net). Specifically, we introduce variation sparsity and ℓ 2, p norm ( 0 < p < 1 ) regularization into the model of the ESI problem and utilize the Alternating Direction Method of Multipliers (ADMM) to iteratively solve this model. Furthermore, by mapping the iterative process into a neural network structure, the proposed VSSI 2 p -Net can optimize all parameters, including the critical p in ℓ 2, p -norm and the variation sparsity operator, in an end-to-end manner with a reasonably sized training set. In this way, VSSI 2 p -Net achieves more flexible prior information integration while retaining the interpretability of traditional methods, so that a more accurate and efficient solution for ESI can be obtained. We compared the performance of VSSI 2 p -Net with several traditional baseline methods and state-of-the-art deep learning methods on synthetic and real datasets. The results show that VSSI 2 p -Net significantly outperforms existing methods in source localization accuracy, spatial range estimation, and imaging speed across various source configurations.

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IROS Conference 2025 Conference Paper

An Online Motion Planning Framework for Navigating Torpedo-shaped Autonomous Underwater Vehicles in Unknown Underwater Environments

Tianyou Yu
Zhaoxuan Dong
Yu Wu
Xingjie Fu

Navigating unknown underwater environments is a significant challenge for autonomous underwater vehicles (AUVs), especially those with torpedo-like shapes. Lacking a prior map, these vehicles rely on real-time sensor data for perception. Although online motion planning addresses this challenge, many existing methods are primarily tested on more maneuverable robots, such as multicopters and ground vehicles, and do not account for the unique kinematics of torpedo-shaped AUVs, such as limited lateral movement, or the need for 3D motion planning. In this paper, we propose an online motion planning system specifically designed for torpedo-shaped AUVs to navigate 3D underwater terrain without prior environmental knowledge. The system employs a receding horizon planning framework to ensure safe navigation by replanning the trajectory when collisions are detected or the planning horizon is reached. For trajectory generation, a search-based method is used and utilizes a 3D Dubins curve heuristic to guide the generation of an optimal 3D trajectory that adheres to the AUV’s kinematic constraints. To further enhance safety and smoothness, gradient-based optimization is applied to refine the trajectory. Experiments in simulated environments validate the proposed method, demonstrating its ability to generate safe trajectories for AUVs in complex and unknown environments. We release our code as an open-source package 1.

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YNIMG Journal 2019 Journal Article

Grouped sparse Bayesian learning for voxel selection in multivoxel pattern analysis of fMRI data

Zhenfu Wen
Tianyou Yu
Zhuliang Yu
Yuanqing Li

Multivoxel pattern analysis (MVPA) methods have been widely applied in recent years to classify human brain states in functional magnetic resonance imaging (fMRI) data analysis. Voxel selection plays an important role in MVPA studies not only because it can improve decoding accuracy but also because it is useful for understanding brain functions. There are many voxel selection methods that have been proposed in fMRI literature. However, most of these methods either overlook the structure information of fMRI data or require additional cross-validation procedures to determine the hyperparameters of the models. In the present work, we proposed a voxel selection method for binary brain decoding called group sparse Bayesian logistic regression (GSBLR). This method utilizes the group sparse property of fMRI data by using a grouped automatic relevance determination (GARD) as a prior for model parameters. All the parameters in the GSBLR can be estimated automatically, thereby avoiding additional cross-validation. Experimental results based on two publicly available fMRI datasets and simulated datasets demonstrate that GSBLR achieved better classification accuracies and yielded more stable solutions than several state-of-the-art methods.

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