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Tong Han

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7 papers

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EAAI Journal 2026 Journal Article

Inferring directed gene regulatory networks from single-cell ribonucleic acid sequencing data via multi-view contrastive learning

Yangyang Meng
Minhao Yao
Tong Han
Huandong Zhao
Zhonghua Liu
Baoshan Ma

Gene regulatory networks (GRNs) play a crucial role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning-based approaches have been proposed to infer GRNs from single-cell transcriptomics data with impressive results. However, these methods do not fully and dynamically adjust the relative importance and high-level features of the node embedding representations of graph models. In addition, GRNs of real species are large-scale networks with directionality and high sparsity, which hinders the advancement of GRN inference. To overcome these limitations, we propose a novel model based on multi-view contrastive learning (MCLGAT) to infer GRNs. MCLGAT is primarily an integration of graph attention network (GAT), multi-view frameworks, and contrastive learning fusion model. We used an adjacency matrix of GRN to generate a direction vector, therefore, MCLGAT can obtain directed gene regulation relationship. Improved GATs optimize attention weights and the multi-view models simultaneously extract the local feature and high-level feature of the nodes in the GRN. The contrastive learning fusion model dynamically adjusts the relative importance and effectively aggregates node embedding representations from both views. In comparisons with 10 state-of-the-art methods, MCLGAT achieved competitive performance on seven benchmark single-cell ribonucleic acid sequencing (scRNA-seq) datasets from four cell lines. Furthermore, potential biomarkers and therapeutic drugs for lung and breast cancer were identified using the candidate regulatory genes inferred by MCLGAT, which provides a theoretical basis for elucidating the gene regulatory mechanism of complex diseases and developing personalized diagnosis and treatment plans.

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JBHI Journal 2025 Journal Article

Completed Feature Disentanglement Learning for Multimodal MRIs Analysis

Tianling Liu
Hongying Liu
Fanhua Shang
Lequan Yu
Tong Han
Liang Wan

Multimodal MRIs play a crucial role in clinical diagnosis and treatment. Feature disentanglement (FD)-based methods, aiming at learning superior feature representations for multimodal data analysis, have achieved significant success in multimodal learning (MML). Typically, existing FD-based methods separate multimodal data into modality-shared and modality-specific features, and employ concatenation or attention mechanisms to integrate these features. However, our preliminary experiments indicate that these methods could lead to a loss of shared information among subsets of modalities when the inputs contain more than two modalities, and such information is critical for prediction accuracy. Furthermore, these methods do not adequately interpret the relationships between the decoupled features at the fusion stage. To address these limitations, we propose a novel Complete Feature Disentanglement (CFD) strategy that recovers the lost information during feature decoupling. Specifically, the CFD strategy not only identifies modality-shared and modality-specific features, but also decouples shared features among subsets of multimodal inputs, termed as modality-partial-shared features. We further introduce a new Dynamic Mixture-of-Experts Fusion (DMF) module that dynamically integrates these decoupled features, by explicitly learning the local-global relationships among the features. The effectiveness of our approach is validated through classification tasks on three multimodal MRI datasets. Extensive experimental results demonstrate that our approach outperforms other state-of-the-art MML methods with obvious margins, showcasing its superior performance.

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YNICL Journal 2025 Journal Article

Reorganization of cortical individualized differential structural covariance network is associated with regional morphometric changes in chronic subcortical stroke

Hongchuan Zhang
Jun Guo
Jingchun Liu
Caihong Wang
Hao Ding
Tong Han
Jingliang Cheng
Chunshui Yu

Patients with chronic subcortical stroke undergo regional and network morphometric reorganizations beyond the lesion site, but the interplay between network and regional reorganization remains poorly understood. We aimed to clarify the reorganization patterns of the individualized differential structural covariance networks (IDSCN) in chronic subcortical stroke and investigate their associations with regional gray matter volume (GMV) changes and functional recovery. Structural MRI from four datasets enrolled 112 patients with chronic subcortical stroke (81 male, age: 55.82 ± 7.79) and 122 matched healthy controls (HC) (74 male; age: 55.28 ± 7.54). Network-based statistics were employed to identify aberrant IDSCN, Spearman correlation was conducted to assess the association between IDSCN and regional GMV alterations, and partial correlation was utilized to investigate the association between abnormal IDSCN and functional recovery. We identified 133 connections with balanced increased and decreased IDSCN. Aberrant IDSCN involved more regions than local GMV alterations, local GMV alteration exhibited intricate correlations with IDSCN, which could explain partly IDSCN reorganization (p < 0.05, corrected). Finally, abnormal IDSCN showed a weak association with long-term clinical recovery (p < 0.01). These findings reinforce the theory of adaptive network reorganization post-stroke and suggest that IDSCN may provide further insights into cortical reorganization and functional rehabilitation beyond regional morphometric measures.

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EAAI Journal 2025 Journal Article

Uncertainty-aware focal loss for object segmentation

Lei Chen
Yang Wang
Jibin Yang
Yunfei Zheng
Tong Han
Bo Zhang
Tieyong Cao

In the loss function of object segmentation models, misclassified pixels whose prediction are opposite to the ground truth and uncertain pixels whose predicted probability is close to 0. 5 are more important for model training. Focusing on misclassified pixels can improve the segmentation accuracy of the model, and focusing on uncertain pixels can help the model to form better decision surfaces. However, existing methods fail to take both types of pixels into account simultaneously. To enhance the learning on these two types of important pixels, the Uncertainty-aware Focal Loss (UFL) is proposed based on the analysis of Uncertainty-aware Loss (UAL). Then, by leveraging the S-shaped property of the sigmoid function, a loss function is constructed that can simultaneously increase the loss and loss derivatives of misclassified and uncertain pixels. In order to solve the gradient vanishing problem of the sigmoid function on well-classified pixels, a regularization constraint term is defined, whose value is the square of predicted probability. Finally, the pixel loss value is dynamically adjusted at different stages of training according to the changes in the contributions of misclassified and uncertain pixels to the model training, which improves the targeted learning for misclassified and uncertain pixels. Experimental results on two different types of network structures and six datasets demonstrate that the proposed method can better segment the uncertain and misclassified pixels. Especially, on the DUT-O dataset, UFL improves mean Intersection over Union (mIoU) by almost 2. 7 % compared to UAL.

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JBHI Journal 2023 Journal Article

Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation

Junting Zhao
Zhaohu Xing
Zhihao Chen
Liang Wan
Tong Han
Huazhu Fu
Lei Zhu

Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based approaches well respect spatial relationships across slices, slice-based methods typically excel at capturing fine local features. Furthermore, there is a wealth of complementary information between their segmentation predictions. Inspired by this observation, we develop an Uncertainty-aware Multi-dimensional Mutual learning framework to learn different dimensional networks simultaneously, each of which provides useful soft labels as supervision to the others, thus effectively improving the generalization ability. Specifically, our framework builds upon a 2D-CNN, a 2. 5D-CNN, and a 3D-CNN, while an uncertainty gating mechanism is leveraged to facilitate the selection of qualified soft labels, so as to ensure the reliability of shared information. The proposed method is a general framework and can be applied to varying backbones. The experimental results on three datasets demonstrate that our method can significantly enhance the performance of the backbone network by notable margins, achieving a Dice metric improvement of 2. 8% on MeniSeg, 1. 4% on IBSR, and 1. 3% on BraTS2020.

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YNICL Journal 2019 Journal Article

Differential involvement of rubral branches in chronic capsular and pontine stroke

Jun Guo
Jingchun Liu
Caihong Wang
Chen Cao
Lejun Fu
Tong Han
Jingliang Cheng
Chunshui Yu

Background and Purpose Early studies have indicated that the cortico-rubro-spinal tracts play important roles in motor dysfunction after stroke. However, the differential involvement of the rubral branches in capsular and pontine stroke, and their associations with the motor impairment are still unknown. Methods The present study recruited 144 chronic stroke patients and 91 normal controls (NC) from three hospitals, including 102 cases with capsular stroke (CS) and 42 cases with pontine stroke (PS). The rubral branches, including bilateral corticorubral tracts (CRT), dentatorubral tracts (DRT), and rubrospinal tracts (RST), and the cortico-spinal tract (CST) were reconstructed based on the dataset of the Human Connectome Project. Group differences in diffusion scalars of each rubral branch were compared, and the associations between the diffusion measures of rubral branches and the Fugl-Meyer assessment (FMA) scores were tested. Results The bilateral CRT of the CS cases showed significantly lower factional anisotropy (FA) than in the NC. The bilateral DRT of the PS cases had lower FA than in the NC. Both CS and PS cases had significantly lower FA of the bilateral RST than the NC. Besides, the stroke patients demonstrated significantly lower FA in bilateral CSTs than the NC. Partial correlation analysis identified significantly positive correlations between the FA of the ipsilesional and CRT and the FMA scores in the CS group, and significantly positive correlations between the FA of the RST bilaterally and the FMA scores in the CS and PS groups. Furthermore, the association between RST integrity and FMA scores still survived after controlling for the effect of the CST. Finally, multiple regression modelling found that rubral tract FA explained 39. 2% of the variance in FMA scores for CS patients, and 48. 8% of the variance in FMA scores for PS patients. Conclusions The bilateral rubral branches were differentially involved in the chronic capsular and pontine stroke, and the impairment severity of each rubral branch was dependent on lesion locations. The integrity of the rubral branches is related to motor impairment in both the chronic capsular and pontine stroke.

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YNICL Journal 2017 Journal Article

Gray matter volume changes in chronic subcortical stroke: A cross-sectional study

Qingqing Diao
Jingchun Liu
Caihong Wang
Chen Cao
Jun Guo
Tong Han
Jingliang Cheng
Xuejun Zhang

This study aimed to investigate the effects of lesion side and degree of motor recovery on gray matter volume (GMV) difference relative to healthy controls in right-handed subcortical stroke. Structural MRI data were collected in 97 patients with chronic subcortical ischemic stroke and 79 healthy controls. Voxel-wise GMV analysis was used to investigate the effects of lesion side and degree of motor recovery on GMV difference in right-handed chronic subcortical stroke patients. Compared with healthy controls, right-lesion patients demonstrated GMV increase (P <0. 05, voxel-wise false discovery rate correction) in the bilateral paracentral lobule (PCL) and supplementary motor area (SMA) and the right middle occipital gyrus (MOG); while left-lesion patients did not exhibit GMV difference under the same threshold. Patients with complete and partial motor recovery showed similar degree of GMV increase in right-lesion patients. However, the motor recovery was correlated with the GMV increase in the bilateral SMA in right-lesion patients. These findings suggest that there exists a lesion-side effect on GMV difference relative to healthy controls in right-handed patients with chronic subcortical stroke. The GMV increase in the SMA may facilitate motor recovery in subcortical stroke patients.

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