Author name cluster

Yaping Yan

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5 papers

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YNICL Journal 2025 Journal Article

Arterial spin labeling MRI based perfusion pattern related to motor dysfunction and L-DOPA reactivity in Parkinson’s disease

Qianshi Zheng
Weijin Yuan
Jiaqi Wen
Jianmei Qin
Chenqing Wu
Haoting Wu
Xiaojie Duanmu
Sijia Tan

OBJECTIVE: Identifying intrinsic pattern of Parkinson's disease (PD) helps to better understand of PD and provide insights to disease identification and treatment monitoring. Here we confirmed the PD-related covariance pattern (PDRP) by using arterial spin labelling technology (ASL-PDRP) and explore its potential for predicting motor progression and levodopa (L-DOPA) reactivity reduction. METHODS: Data from an original cohort of 179 PD and 62 normal controls (NC) and a validation cohort including 36 PD and 19 NC to construct and validate the ASL-PDRP. The correlations between the pattern and motor symptoms were analyzed cross-sectionally and longitudinally (71 PD owned longitudinal data) with hierarchical linear regression analysis. Kaplan-Meier analysis was conducted in 54 L-DOPA-managed PD patients to predict the levodopa reactivity reduction. RESULTS: The first principal component was predominantly recognized as the ASL-PDRP, with its expression being higher in PD than NC in both sets (original: P = 0.017, AUC = 0.598; validation: P = 0.024, AUC = 0.661). The pattern expression was associated with UPDRS III (P = 0.006) and sub-symptoms (axial: P < 0.001; rigidity: P = 0.003; bradykinesia: P = 0.015) at baseline. The ASL-PDRP could predict the progression of UPDRS III (P = 0.021, β = 4.930). Higher expression of the pattern had slower rate of levodopa reactivity reduction in PD patients with axial symptom (P = 0.031). CONCLUSION: The identified ASL-PDRP may have potential for characterizing PD with the ability to predict motor progression and L-DOPA reactivity reduction.

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AAAI Conference 2025 Conference Paper

Exploring the Relationship Between Samples and Masks for Robust Defect Localization

Jiang Lin
Hui Xue
Fanxiu Sun
Yaping Yan

Defect detection aims to detect and localize regions out of the normal distribution. The previous approaches often explicitly incorporate the defect detection concept, such as by utilizing self-supervised ground truth or manually defined feature comparison. The aforementioned processes involve modeling the distribution of normal samples, and they rely on the modeled normality for accurate inference. This reliance may hinder their ability to generalize to unseen test scenarios or the test set that deviates from the training distribution. In this paper, we propose a one-stage framework that detects defective patterns directly without the modeling process. This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic correction mechanism that allows the network to self-correct. In training, explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping. Experimental results show that the proposed method performs superior in comparison with the previous SOTA methods in various test scenarios.

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AAAI Conference 2024 Conference Paper

A Comprehensive Augmentation Framework for Anomaly Detection

Jiang Lin
Yaping Yan

Data augmentation methods are commonly integrated into the training of anomaly detection models. Previous approaches have primarily focused on replicating real-world anomalies or enhancing diversity, without considering that the standard of anomaly varies across different classes, potentially leading to a biased training distribution. This paper analyzes crucial traits of simulated anomalies that contribute to the training of reconstructive networks and condenses them into several methods, thus creating a comprehensive framework by selectively utilizing appropriate combinations. Furthermore, we integrate this framework with a reconstruction-based approach and concurrently propose a split training strategy that alleviates the overfitting issue while avoiding introducing interference to the reconstruction process. The evaluations conducted on the MVTec anomaly detection dataset demonstrate that our method outperforms the previous state-of-the-art approach, particularly in terms of object classes. We also generate a simulated dataset comprising anomalies with diverse characteristics, and experimental results demonstrate that our approach exhibits promising potential for generalizing effectively to various unseen anomalies encountered in real-world scenarios.

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AAAI Conference 2023 Conference Paper

ParaFormer: Parallel Attention Transformer for Efficient Feature Matching

Xiaoyong Lu
Yaping Yan
Bin Kang
Songlin Du

Heavy computation is a bottleneck limiting deep-learning-based feature matching algorithms to be applied in many real-time applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks, since sparse keypoint based descriptors are expected to be matched. This paper tackles this problem and proposes two concepts: 1) a novel parallel attention model entitled ParaFormer and 2) a graph based U-Net architecture with attentional pooling. First, ParaFormer fuses features and keypoint positions through the concept of amplitude and phase, and integrates self- and cross-attention in a parallel manner which achieves a win-win performance in terms of accuracy and efficiency. Second, with U-Net architecture and proposed attentional pooling, the ParaFormer-U variant significantly reduces computational complexity, and minimize performance loss caused by downsampling. Sufficient experiments on various applications, including homography estimation, pose estimation, and image matching, demonstrate that ParaFormer achieves state-of-the-art performance while maintaining high efficiency. The efficient ParaFormer-U variant achieves comparable performance with less than 50% FLOPs of the existing attention-based models.

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YNIMG Journal 2022 Journal Article

Altered brain iron depositions from aging to Parkinson's disease and Alzheimer's disease: A quantitative susceptibility mapping study

Xiaojun Guan
Tao Guo
Cheng Zhou
Jingjing Wu
Qingze Zeng
Kaicheng Li
Xiao Luo
Xueqin Bai

Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e. g. , Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p < 0. 01). pPD showed a high spatial variation of iron distribution in the subcortical nuclei except for substantia nigra (SN); and iron deposition in SN and red nucleus (RN) were progressively increased from pPD to cPD (p < 0. 01). AD showed significantly higher iron deposition in caudate and putamen with higher spatial variation compared with old adults, pPD and cPD (p < 0. 01), and significant iron deposition in SN compared with old adults (p < 0. 01). Also, linear regression models had significances in predicting motor score in pPD and cPD (Rmean = 0. 443, Ppermutation = 0. 001) and cognition score in MCI and AD (Rmean = 0. 243, Ppermutation = 0. 037). In conclusion, progressive iron deposition in the SN and RN may characterize PD-related neurodegeneration, namely aging to cPD through pPD. On the other hand, extreme iron deposition in the caudate and putamen may characterize AD-related neurodegeneration.

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