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Lin Lu

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13 papers

2 author rows

JBHI Journal 2026 Journal Article

MFE-Former: Disentangling Emotion-Identity Dynamics via Self-Supervised Learning for Enhancing Speech-Driven Depression Detection

Hao Wang
Jiayu Ye
Yanhong Yu
Lin Lu
Lin Yuan
Qingxiang Wang

Acoustic features are crucial behavioral indicators for depression detection. However, prior speech-based depression detection methods often overlook the variability of emotional patterns across samples, leading to interference from speaker identity and hindering the effective extraction of emotional changes. To address this limitation, we developed the Emotional Word Reading Experiment (EWRE) and introduced a method combining self-supervised and supervised learning for depression detection from speech called MFE-Former. First, we generate fine-grained emotional representations for response segments by computing cosine similarity between intra-sample and inter-sample contexts. Concurrently, orthogonality constraints decouple identity information from emotional features, while a Transformer decoder reconstructs spectral structures to improve sensitivity to depression-related emotional patterns. Next, we propose a multi-scale emotion change perception module and a Bernoulli distribution-based joint decision module integrate multi-level information for depression detection. By enhancing the distribution differences among positive, neutral, and negative emotional features, we find that patients with depression are more inclined to express negative emotions, whereas healthy individuals express more positive emotions. The experimental results on EWRE and AVEC 2014 show that MFE-Former outperforms state-of-the-art temporal methods under conditions of variability in emotional patterns across samples.

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ICLR Conference 2025 Conference Paper

Conditional Testing based on Localized Conformal p-values

Xiaoyang Wu
Lin Lu
Zhaojun Wang
Changliang Zou

In this paper, we address conditional testing problems through the conformal inference framework. We define the localized conformal $p$-values by inverting prediction intervals and prove their theoretical properties. These defined $p$-values are then applied to several conditional testing problems to illustrate their practicality. Firstly, we propose a conditional outlier detection procedure to test for outliers in the conditional distribution with finite-sample false discovery rate (FDR) control. We also introduce a novel conditional label screening problem with the goal of screening multivariate response variables and propose a screening procedure to control the family-wise error rate (FWER). Finally, we consider the two-sample conditional distribution test and define a weighted U-statistic through the aggregation of localized $p$-values. Numerical simulations and real-data examples validate the superior performance of our proposed strategies.

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EAAI Journal 2025 Journal Article

Depression and anxiety detection method based on serialized facial expression imitation

Lin Lu
Yan Jiang
Xingyun Li
Hao Wang
Qingzhi Zou
Qingxiang Wang

Facial recognition techniques are widely employed for automatic detection of depression and anxiety. However, current studies overlook the impact of varying spatial resolutions on model performance and lack a mechanism to share attention regions across sequential data. To advance research in this area, we conducted the Voluntary Facial Expression Mimicry Experiment (VFEM) and constructed the VFEM dataset. We also introduce the SFE-Former, a sequential facial expression recognition model designed for detecting depression and anxiety. SFE-Former features a mechanism that shares attention regions across sequence data, allowing each data point to enhance its features by leveraging shared information. Additionally, the model integrates features from different scales using fusion and weighting strategies. The experimental results indicate that SFE-Former achieved impressive accuracy rate: 0. 893 for depression detection, 0. 889 for anxiety detection, and 0. 780 for co-occurrence detection of depression and anxiety. Meanwhile, SFE-Former also obtained state-of-the-art (SOAT) results on AVEC2014 dataset. This work can enhance the accuracy of identifying patients with depression and anxiety, providing doctors with reliable auxiliary diagnosis. The source code for SFE-Former is accessible at https: //github. com/lulin-6k/SFE-Former.

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JBHI Journal 2025 Journal Article

Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis

Baiying Lei
Gege Cai
Yun Zhu
Tianfu Wang
Lei Dong
Cheng Zhao
Xinzhi Hu
Huijun Zhu

The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of patients. Magnetic resonance imaging (MRI) has proven to be an effective tool for the early detection of sellar region tumors. However, the existing sellar region tumor diagnosis still remains challenging due to the small amount of dataset and data imbalance. To overcome these challenges, we propose a novel self-supervised multi-scale multi-modal graph pool Transformer (MMGPT) network that can enhance the multi-modal fusion of small and imbalanced MRI data of sellar region tumors. MMGPT can strengthen feature interaction between multi-modal images, which makes our model more robust. A contrastive learning equipped auto-encoder (CAE) via self-supervised learning (SSL) is adopted to learn more detailed information between different samples. The proposed CAE transfers the pre-trained knowledge to the downstream tasks. Finally, a hybrid loss is equipped to relieve the performance degradation caused by data imbalance. The experimental results show that the proposed method outperforms state-of-the-art methods and obtains higher accuracy and AUC in the classification of sellar region tumors.

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NeurIPS Conference 2024 Conference Paper

Real-Time Selection Under General Constraints via Predictive Inference

Yuyang Huo
Lin Lu
Haojie Ren
Changliang Zou

Real-time decision-making gets more attention in the big data era. Here, we consider the problem of sample selection in the online setting, where one encounters a possibly infinite sequence of individuals collected over time with covariate information available. The goal is to select samples of interest that are characterized by their unobserved responses until the user-specified stopping time. We derive a new decision rule that enables us to find more preferable samples that meet practical requirements by simultaneously controlling two types of general constraints: individual and interactive constraints, which include the widely utilized False Selection Rate (FSR), cost limitations, and diversity of selected samples. The key elements of our approach involve quantifying the uncertainty of response predictions via predictive inference and addressing individual and interactive constraints in a sequential manner. Theoretical and numerical results demonstrate the effectiveness of the proposed method in controlling both individual and interactive constraints.

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YNICL Journal 2024 Journal Article

Right superior frontal gyrus: A potential neuroimaging biomarker for predicting short-term efficacy in schizophrenia

Yongfeng Yang
Xueyan Jin
Yongjiang Xue
Xue Li
Yi Chen
Ning Kang
Wei Yan
Peng Li

Antipsychotic drug treatment for schizophrenia (SZ) can alter brain structure and function, but it is unclear if specific regional changes are associated with treatment outcome. Therefore, we examined the effects of antipsychotic drug treatment on regional grey matter (GM) density, white matter (WM) density, and functional connectivity (FC) as well as associations between regional changes and treatment efficacy. SZ patients (n = 163) and health controls (HCs) (n = 131) were examined by structural magnetic resonance imaging (sMRI) at baseline, and a subset of SZ patients (n = 77) were re-examined after 8 weeks of second-generation antipsychotic treatment to assess changes in regional GM and WM density. In addition, 88 SZ patients and 81 HCs were examined by resting-state functional MRI (rs-fMRI) at baseline and the patients were re-examined post-treatment to examine FC changes. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) were applied to measure psychiatric symptoms and cognitive impairments in SZ. SZ patients were then stratified into response and non-response groups according to PANSS score change (≥50 % decrease or <50 % decrease, respectively). The GM density of the right cingulate gyrus, WM density of the right superior frontal gyrus (SFG) plus 5 other WM tracts were reduced in the response group compared to the non-response group. The FC values between the right anterior cingulate and paracingulate gyrus and left thalamus were reduced in the entire SZ group (n = 88) after treatment, while FC between the right inferior temporal gyrus (ITG) and right medial superior frontal gyrus (SFGmed) was increased in the response group. There were no significant changes in regional FC among the non-response group after treatment and no correlations with symptom or cognition test scores. These findings suggest that the right SFG is a critical target of antipsychotic drugs and that WM density and FC alterations within this region could be used as potential indicators in predicting the treatment outcome of antipsychotics of SZ.

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EAAI Journal 2023 Journal Article

Federated clustering for recognizing driving styles from private trajectories

Lin Lu
Yao Lin
YUAN WEN
Jinxiong Zhu
Shengwu Xiong

Driving style recognition of real-world drivers is beneficial for various reasons, such as safe and economic driving, auto-insurance and designing autonomous systems. A common way to achieve this goal is to group drivers using clustering methods according to their trajectory data. However, the conventional model training process is centralized, where all drivers’ private trajectories are collected and shared, which has resulted in privacy concerns. Considering that the driving data are sourced from various vehicles and have a decentralized distribution, we introduce a federated clustering approach for privacy-preserving driving style recognition. The method preserves the trajectory data in edge devices, such as connected vehicles or roadside units, and allows only the exchange of model parameters and not raw, sensitive data under the coordination of a central server to meet the privacy protection requirements. To address the clustering heterogeneous challenge posed by the imbalanced distribution of trajectory data in this setting, we combine local Bayesian Gaussian mixture and global weighted K-means to output high-quality global initialization centers. Then, novel local training and global aggregation strategies are proposed to ensure the convergence of training and improve the performance of the final global model. Through comparison experiments on benchmark and real-world datasets, we conclude that this method outperforms existing methods.

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EAAI Journal 2021 Journal Article

A bi-level distribution mixture framework for unsupervised driving performance evaluation from naturalistic truck driving data

Lin Lu
Shengwu Xiong
Yaxiong Chen

Driving performance evaluations can contribute to fleet management and lead to safer and more economical driving conditions for manned or driverless fleet vehicles. One approach to driving performance evaluation involves quantitative mapping or categorical labeling of skill levels and categorizing of driving patterns from extraordinarily mild to the most aggressive. This paper presents a big data system for driving performance evaluations of drivers and trips using a probabilistic framework. The proposed framework combines a feature mixture model for scoring driving performance through defined objective comparison criteria and a latent style mixture model for classifying drivers by the main driving styles they exhibit. To demonstrate the effectiveness of the proposed models, we perform both quantitative and qualitative experiments. The results show that the former produces an interpretable and normal scorecard model, while the latter helps build an improved clustering model that represents enhanced driver behavior.

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YNICL Journal 2021 Journal Article

Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score

Ke Hu
Meng Wang
Yong Liu
Hao Yan
Ming Song
Jun Chen
Yunchun Chen
Huaning Wang

Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.

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YNICL Journal 2020 Journal Article

Disruption of the structural and functional connectivity of the frontoparietal network underlies symptomatic anxiety in late-life depression

Hui Li
Xiao Lin
Lin Liu
Sizhen Su
Ximei Zhu
Yongbo Zheng
Weizhen Huang
Jianyu Que

The present study investigated functional connectivity and white matter integrity of the fronto-parietal network (FPN) to reveal the neural mechanisms that underlie late-life depression (LLD). Fifty patients with LLD and 40 non-depressed controls were included in the study. A multi-parametric approach was used by applying independent component analysis (ICA) to estimate functional connectivity of the FPN and by applying tract-based spatial statistics to examine white-matter integrity in tracts to the FPN. Patients with LLD exhibited functional abnormalities in the right inferior frontal gyrus, middle frontal gyrus, and inferior parietal gyrus and lower white matter fractional anisotropy in the right inferior fronto-occipital fasciculus, anterior thalamic radiation, and uncinate fasciculus. Alterations of functional connectivity and white matter fractional anisotropy in these regions were negatively correlated with the severity of symptomatic anxiety in LLD patients. The right inferior frontal gyrus might be a crucial hub in transferring information between these abnormal regions. Significant correlations were found between anxiety symptoms and brain alterations, suggesting that impairments in the FPN network might be involved in symptomatic anxiety in elderly individuals with depression.

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JBHI Journal 2019 Journal Article

Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography

Yacheng Ren
Jingchen Ma
Junfeng Xiong
Yi Chen
Lin Lu
Jun Zhao

Computer-aided detection (CAD) systems can assist radiologists in reducing the interpretation time and improving the detection results in computed tomographic colonography (CTC). However, existing false positives (FPs) impair the advantages of CAD systems. This study aims to develop new morphological features for the FP reduction while maintaining high detection sensitivity. Volumetric feature maps are computed for each polyp candidate by using three-dimensional (3-D) geodesic distance transformation, circular transformation (CcT), and quantized convergence index (QCI) filters. Then, new morphological features are developed based on the curvature, fractal dimension, and volumetric feature maps. To the best of our knowledge, we are also the first to develop 3-D CcT and QCI filters specifically for colonic polyps. The new morphological features were evaluated to reduce the FPs by using 456 oral contrast-enhanced CT scans from 228 patients with 130 polyps ≥5 mm. For comparison, the well-defined features from our previous work were used to generate a baseline reference. The additional use of the new morphological features reduced the FP rate from 4. 2 to 2. 0 FPs per scan (i. e. , 52. 4% FP reduction percentage) at 96. 2% by-polyp sensitivity and from 4. 5 to 2. 1 FPs per scan (i. e. , 53. 3% FP reduction percentage) at 93. 9% per-scan sensitivity for polyps ≥5 mm. Experimental results indicate that the new morphological features can effectively reduce the FP rate without sacrificing detection sensitivity. We believe that the newly developed morphological features would advance the CAD systems to assist radiologists in interpreting CTC images.

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YNICL Journal 2018 Journal Article

Enhanced temporal variability of amygdala-frontal functional connectivity in patients with schizophrenia

Jing-Li Yue
Peng Li
Le Shi
Xiao Lin
Hong-Qiang Sun
Lin Lu

Background: The "dysconnectivity hypothesis" was proposed 20 years ago. It characterized schizophrenia as a disorder with dysfunctional connectivity across a large range of distributed brain areas. Resting-state functional magnetic resonance imaging (rsfMRI) data have supported this theory. Previous studies revealed that the amygdala might be responsible for the emotion regulation-related symptoms of schizophrenia. However, conventional methods oversimplified brain activities by assuming that it remained static throughout the entire scan duration, which may explain why inconsistent results have been reported for the same brain region. Methods: An emerging technique is sliding time window analysis, which is used to describe functional connectivity based on the temporal variability of regions of interest (e.g., amygdala) in patients with schizophrenia. Conventional analysis of the static functional connectivity between the amygdala and whole brain was also conducted. Results: Static functional connectivity between the amygdala and orbitofrontal region was impaired in patients with schizophrenia. The variability of connectivity between the amygdala and medial prefrontal cortex was enhanced (i.e., greater dynamics) in patients with schizophrenia. A negative relationship was found between the variability of connectivity and information processing efficiency. A positive correlation was found between the variability of connectivity and symptom severity. Conclusion: The findings suggest that schizophrenia was related to abnormal patterns of fluctuating communication among brain areas that are involved in emotion regulations. Unveiling the temporal properties of functional connectivity could disentangle the inconsistent results of previous functional connectivity studies.

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ICRA Conference 1988 Conference Paper

Obstacle avoidance path planning of a manipulator

Kai Xia
Song Jiang
Lin Lu

This basic robot planning system chooses a motion path to avoid collision with obstacles in workspace. With a camera above the workspace the computer vision can give a simple description of the world model. The presented algorithm transfers a Cartesian description of obstacles into the coordinate of the first three joints of a manipulator. The freespace is described hierarchically by a two-level representation, and a two-level optimization is used for the path planning. Experiments show the whole system can be realized by an IBM-PC with very small memory. The average computation time for one obstacle is 10 s. >

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