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Congcong Liu

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8 papers

2 author rows

EAAI Journal 2025 Journal Article

Kidney stone and tumor segmentation by analyzing medical images using deep learning technique

Fangfang Ye
Congcong Liu
Jinming Wang
Qingrong Sun
Somia Asklany

The segmentation of kidney tumors is a critical activity in medical imaging since it aids in effective diagnosis, treatment, and follow-ups of renal disorders. However, the segmentation process faces challenges related to variability in the tumor's size, shape, and intensity, as well as the noise and artifacts in medical images. This study aims to address the challenge of designing an effective and automated Deep Neural Model (DNM) analysis for Computed Tomography (CT) images of kidney stones and tumor segmentation, which is more accurate, faster, and more efficient than current state-of-the-art models. The DNM utilizes the U-Net structure to extract cross-scale features from the CT images. The extracted features are further explored with the aid of a transformer model, which identifies and extracts local and global context features to enhance mask segmentation efficiency. The obtained results reveal a considerable enhancement in segmentation results, achieving an 8 % increase in the Dice similarity coefficient (DSC) compared to standard techniques. This approach primarily focuses on segmenting renal cell carcinoma, a pathology commonly associated with kidney tumors, and demonstrates strong potential to assist clinical diagnosis, surgical planning, and treatment monitoring in nephrology, contributing to improved assessment and management of chronic kidney diseases (CKD). The proposed DNM model increases the precision ratio by 98. 89 %, the recall ratio by 97. 12 %, the accuracy ratio by 98. 43 %, the F1-score ratio by 98. 5 %, and the IoU by 99. 18 % compared to existing models.

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AAAI Conference 2024 Conference Paper

Generalize for Future: Slow and Fast Trajectory Learning for CTR Prediction

Jian Zhu
Congcong Liu
Xue Jiang
Changping Peng
Zhangang Lin
Jingping Shao

Deep neural networks (DNNs) have achieved significant advancements in click-through rate (CTR) prediction by demonstrating strong generalization on training data. However, in real-world scenarios, the assumption of independent and identically distributed (i.i.d.) conditions, which is fundamental to this problem, is often violated due to temporal distribution shifts. This violation can lead to suboptimal model performance when optimizing empirical risk without access to future data, resulting in overfitting on the training data and convergence to a single sharp minimum. To address this challenge, we propose a novel model updating framework called Slow and Fast Trajectory Learning (SFTL) network. SFTL aims to mitigate the discrepancy between past and future domains while quickly adapting to recent changes in small temporal drifts. This mechanism entails two interactions among three complementary learners: (i) the Working Learner, which updates model parameters using modern optimizers (e.g., Adam, Adagrad) and serves as the primary learner in the recommendation system, (ii) the Slow Learner, which is updated in each temporal domain by directly assigning the model weights of the working learner, and (iii) the Fast Learner, which is updated in each iteration by assigning exponentially moving average weights of the working learner. Additionally, we propose a novel rank-based trajectory loss to facilitate interaction between the working learner and trajectory learner, aiming to adapt to temporal drift and enhance performance in the current domain compared to the past. We provide theoretical understanding and conduct extensive experiments on real-world CTR prediction datasets to validate the effectiveness and efficiency of SFTL in terms of both convergence speed and model performance. The results demonstrate the superiority of SFTL over existing approaches.

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IROS Conference 2022 Conference Paper

HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction

Yuying Chen
Congcong Liu
Xiaodong Mei 0001
Bertram E. Shi
Ming Liu 0001

Pedestrian trajectory prediction is of great importance for downstream tasks, such as autonomous driving and mobile robot navigation. Realistic models of the social interactions within the crowd is crucial for accurate pedestrian trajectory prediction. However, most existing methods do not capture group level interactions well, focusing only on pairwise interactions and neglecting group-wise interactions. In this work, we propose a hierarchical graph convolutional network, HGCN-GJS, for trajectory prediction which well leverages group level interactions within the crowd. Furthermore, we introduce a joint sampling scheme that captures co-dependencies between pedestrian trajectories during trajectory generation. Based on group information, this scheme ensures that generated trajectories within each group are consistent with each other, but enables different groups to act more independently. We demonstrate that our proposed network achieves state of the art performance on all datasets we have considered.

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ICRA Conference 2021 Conference Paper

AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention

Congcong Liu
Yuying Chen
Ming Liu 0002
Bertram E. Shi

Pedestrian trajectory prediction is a critical yet challenging task especially for crowded scenes. We suggest that introducing an attention mechanism to infer the importance of different neighbors is critical for accurate trajectory prediction in scenes with varying crowd size. In this work, we propose a novel method, AVGCN, for trajectory prediction utilizing graph convolutional networks (GCN) based on human attention (A denotes attention, V denotes visual field constraints). First, we train an attention network that estimates the importance of neighboring pedestrians, using gaze data collected as subjects perform a bird’s eye view crowd navigation task. Then, we incorporate the learned attention weights modulated by constraints on the pedestrian’s visual field into a trajectory prediction network that uses a GCN to aggregate information from neighbors efficiently. AVGCN also considers the stochastic nature of pedestrian trajectories by taking advantage of variational trajectory prediction. Our approach achieves state-of-the-art performance on several trajectory prediction benchmarks, and the lowest average prediction error over all considered benchmarks.

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YNICL Journal 2021 Journal Article

Disorder- and emotional context-specific neurofunctional alterations during inhibitory control in generalized anxiety and major depressive disorder

Congcong Liu
Jing Dai
Yuanshu Chen
Ziyu Qi
Fei Xin
Qian Zhuang
Xinqi Zhou
Feng Zhou

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are highly debilitating and often co-morbid disorders. The disorders exhibit partly overlapping dysregulations on the behavioral and neurofunctional level. The determination of disorder-specific behavioral and neurofunctional dysregulations may therefore promote neuro-mechanistic and diagnostic specificity. In order to determine disorder-specific alterations in the domain of emotion-cognition interactions the present study examined emotional context-specific inhibitory control in treatment-naïve MDD (n = 37) and GAD (n = 35) patients and healthy controls (n = 35). On the behavioral level MDD but not GAD exhibited impaired inhibitory control irrespective of emotional context. On the neural level, MDD-specific attenuated recruitment of inferior/medial parietal, posterior frontal, and mid-cingulate regions during inhibitory control were found during the negative context. GAD exhibited a stronger engagement of the left dorsolateral prefrontal cortex relative to MDD. Overall the findings from the present study suggest disorder- and emotional context-specific behavioral and neurofunctional inhibitory control dysregulations in major depression and may point to a depression-specific neuropathological and diagnostic marker.

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IROS Conference 2019 Conference Paper

Gaze Training by Modulated Dropout Improves Imitation Learning

Yuying Chen
Congcong Liu
Lei Tai
Ming Liu 0001
Bertram E. Shi

Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving. The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert’s behavior. Typically, a convolutional neural network learns to predict the steering commands from raw driver-view images by mimicking the behaviors of human drivers. However, there are other cues, such as gaze behavior, available from human drivers that have yet to be exploited. Previous researches have shown that novice human learners can benefit from observing experts’ gaze patterns. We present here that deep neural networks can also profit from this. We propose a method, gaze-modulated dropout, for integrating this gaze information into a deep driving network implicitly rather than as an additional input. Our experimental results demonstrate that gaze-modulated dropout enhances the generalization capability of the network to unseen scenes. Prediction error in steering commands is reduced by 23. 5% compared to uniform dropout. Running closed loop in the simulator, the gaze-modulated dropout net increased the average distance travelled between infractions by 58. 5%. Consistent with these results, the gazemodulated dropout net shows lower model uncertainty.

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IROS Conference 2019 Conference Paper

Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective

Lei Tai
Peng Yun
Yuying Chen
Congcong Liu
Haoyang Ye
Ming Liu 0001

End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually directly applied without considering the uncertainty of the input data. Such kind of policies may bring dramatical damage when applied in the real world. In this paper, we follow the recent real-to-sim pipeline by translating the testing world image back to the training domain when using the trained policy. In the translating process, a stochastic generator is used to generate various images stylized under the training domain randomly or directionally. Based on those translated images, the trained uncertainty-aware imitation learning policy would output both the predicted action and the data uncertainty motivated by the aleatoric loss function. Through the uncertainty-aware imitation learning policy, we can easily choose the safest one with the lowest uncertainty among the generated images. Experiments in the Carla navigation benchmark show that our strategy outperforms previous methods, especially in dynamic environments.

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YNIMG Journal 2014 Journal Article

Linking inter-individual differences in the conflict adaptation effect to spontaneous brain activity

Ting Wang
Zhencai Chen
Guang Zhao
Glenn Hitchman
Congcong Liu
Xiaoyue Zhao
Yijun Liu
Antao Chen

Conflict adaptation has been widely researched in normal and clinical populations. There are large individual differences in conflict adaptation, and it has been linked to the schizotypal trait. However, no study to date has examined how individual differences in spontaneous brain activity are related to behavioral conflict adaptation (performance). Resting-state functional magnetic resonance imaging (RS-fMRI) is a promising tool to investigate this issue. The present study evaluated the regional homogeneity (ReHo) of RS-fMRI signals in order to explore the neural basis of individual differences in conflict adaptation across two independent samples comprising a total of 67 normal subjects. A partial correlation analysis was carried out to examine the relationship between ReHo and behavioral conflict adaptation, while controlling for reaction time, standard deviation and flanker interference effects. This analysis was conducted on 39 subjects' data (sample 1); the results showed significant positive correlations in the left dorsolateral prefrontal cortex (DLPFC) and left ventrolateral prefrontal cortex. We then conducted a test-validation procedure on the remaining 28 subjects' data (sample 2) to examine the reliability of the results. Regions of interest were defined based on the correlation results. Regression analysis showed that variability in ReHo values in the DLPFC accounted for 48% of the individual differences in the conflict adaptation effect in sample 2. The present findings provide further support for the importance of the DLPFC in the conflict adaptation process. More importantly, we demonstrated that ReHo of RS-fMRI signals in the DLPFC can predict behavioral performance in conflict adaptation, which provides potential biomarkers for the early detection of cognitive control deterioration.

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