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Ying Tang

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6 papers
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6

YNIMG Journal 2026 Journal Article

Glymphatic system impairment in Wilson’s disease: An overlooked mechanism for neurological dysfunction?

  • Gaiying Li
  • Siyuan Fang
  • Yupeng Wu
  • Xiangzhen Yuan
  • Ying Tang
  • Xiaozhong Jing
  • Luguang Chen
  • Xiaoping Wang

BACKGROUND: Mounting evidence highlights the critical role of the brain's glymphatic system in cerebral waste clearance, yet its alterations in Wilson's disease (WD) remain unclear. This study aimed to systematically evaluate structural and functional alterations of the glymphatic system across WD clinical phenotypes and their associations with neurological impairment. METHODS: ) volume, and free water-eliminated diffusion tensor imaging analysis along the perivascular space (FWE-DTI-ALPS) index. Group differences were analyzed using ANCOVA, post hoc t-tests, and receiver operating characteristic analyses. Partial correlation analyses were performed to examine associations between MRI and clinical parameters. RESULTS: volume was inversely associated with FWE-DTI-ALPS (r = -0.320). Clinically, ChP enlargement correlated with higher urinary copper levels, whereas fractional anisotropy values, both before and after free water correction, were negatively correlated with serum iron. CONCLUSIONS: These findings provide preliminary imaging evidence of alterations in glymphatic-related MRI markers in WD and suggest that these markers may help differentiate neurological from hepatic phenotypes in research settings. In addition, we emphasize that the observed ALPS changes should be interpreted cautiously, and future longitudinal and multi-shell studies are required before clinical translation can be considered.

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EAAI Journal 2025 Journal Article

Flow decomposition based spatial–temporal filtering self-attention networks for traffic flow forecasting

  • Ying Tang
  • Dawei Wu
  • Zhetao Han

Traffic prediction is essential for intelligent transportation systems but challenging due to complex spatial–temporal dependencies. Current methods often overlook data entanglement caused by stable flow sequences and traffic events, and attention mechanisms may introduce irrelevant spatial information. In this paper, we propose FDFSAN (Flow Decomposition based spatial–temporal Filtering Self-Attention Networks), which addresses these issues by decomposing traffic flow data into stationary and sudden components modeled through a dual-channel spatial–temporal network. Our filtering self-attention mechanism captures both temporal and spatial dependencies, integrating information from nearby and distant roads while minimizing irrelevant spatial noise. Extensive experiments on four real-world datasets show that FDFSAN outperforms state-of-the-art methods, making it suitable for urban traffic networks with dynamic spatial correlations and anomalies.

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AAAI Conference 2025 Conference Paper

Temporal Coherent Object Flow for Multi-Object Tracking

  • Zikai Song
  • Run Luo
  • Lintao Ma
  • Ying Tang
  • Yi-Ping Phoebe Chen
  • Junqing Yu
  • Wei Yang

Multi-object tracking is a challenging vision task that requires simultaneous reasoning about object detection and object association. Conventional solutions use frame as the basic unit and typically rely on a motion predictor that exploits the appearance features to associate detected candidates, leading to insufficient adaptability to long-term associations. In this study, we propose a section-based multi-object tracking approach that integrates a temporal coherent Object Flow Tracker (OFTrack), capable of achieving simultaneous multi-frame tracking by treating multiple consecutive frames as the basic processing unit, denoted as a “section”. Our OFTrack boosts the optical flow to the object flow by employing object perception and section-based motion estimation strategies. Object perception adopts object-aware sampling and scale-aware correlation to enable precise target discrimination. Motion estimation models the correlation of different objects in multi-frames via specialized temporal-spatial attention to achieve robust association in very long videos. Additionally, to address the oscillation of unpredictable trajectories in multi-frame estimation, we have designed temporal coherent enhancement including the trajectory masking pre-training and the smoothing constraint on trajectory curves. Comprehensive experiments on several widely used benchmarks demonstrate the superior performance of our approach.

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JBHI Journal 2022 Journal Article

A Deep Learning Method for Breast Cancer Classification in the Pathology Images

  • Min Liu
  • Lanlan Hu
  • Ying Tang
  • Chu Wang
  • Yu He
  • Chunyan Zeng
  • Kun Lin
  • Zhizi He

Objective: Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, GoogleNet and so on, are prone to overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classification, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distributions and make the predictions suitable for uniform distributions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generalization capabilities make it suitable for histopathology clinical computer-aided diagnosis systems.

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ICRA Conference 2006 Conference Paper

Size Discrimination in Haptic Teleoperation - Influence of Teleoperator Stiffness

  • Göran A. V. Christiansson
  • Ying Tang
  • Richard Quint van der Linde

The quality of a teleoperation system depends on a combination of device characteristics and human perception. This paper presents a study on the relationship between human size discrimination capabilities and teleoperator stiffness for a finger grip grasp task. The teleoperator stiffness was varied in a wide range, and for each setting the human size discrimination capabilities was measured. The teleoperator grasp performance was also compared with two reference conditions: bare hands and fingers in brackets. It was found that the teleoperator stiffness has no measurable influence on operator performance. It was also found that humans surprisingly perform equally well using the teleoperator as with bare hands

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