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学者姓名：连盛
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Cardiovascular disease stands as the leading cause of death globally, and substantial research has revealed its close correlation with the distribution of epicardial adipose tissue (EAT). Moreover, existing studies have demonstrated that EAT exhibits significant differences in distribution patterns and pathophysiological roles across various anatomical regions of the heart. Therefore, the quantitative analysis of EAT at different cardiac locations is crucial, and fine-grained segmentation of EAT via cardiac CT is an efficient method for obtaining the corresponding metrics. The existing computer-aided segmentation approaches typically treat EAT as a unified whole, which fails to meet the demands of nuanced diagnostics, and faces challenges such as class imbalance, thin structures, and anatomical variation, leading to low segmentation accuracy, limiting its application in cardiovascular disease risk assessment. To address these issues, we extend the existing segmentation strategy to the fine-grained segmentation of the left ventricle- (LV-), right ventricle- (RV-), and peri-atrium- (PA-) EAT, and propose the PRAEE framework based on position priors and edge enhancement. The core innovations of the proposed method are as follows: (1)Position-Prior Regularization: Considering the spatial distribution patterns of EAT in different anatomical regions, we design a regularization module that incorporates prior knowledge of typical spatial locations of various types of EAT as auxiliary constraints. This mechanism effectively guides the model to more accurately localize and differentiate EAT across anatomical regions, enabling an initial segmentation.(2)Adaptive Edge Enhancement: To further improve segmentation accuracy, we develop an edge enhancement module that explicitly extracts critical edge information through morphological operations and integrates it into the network architecture, significantly refining segmentation along boundary regions. Our approach has been validated on both a self-collected EAT dataset and the publicly available ACDC and MM-WHS datasets, demonstrating its effectiveness in enhancing fine-grained discrimination and edge detail performance.
Keyword ：
Cardiac CT images Cardiac CT images Epicardial adipose tissue (EAT) Epicardial adipose tissue (EAT) Fine-grained segmentation Fine-grained segmentation
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| GB/T 7714 | Lian, Sheng , Yuan, Qinghe , Su, Qiong et al. Fine-grained epicardial adipose tissue segmentation in cardiac CT images with position priors and edge enhancement [J]. | BIOMEDICAL ENGINEERING LETTERS , 2025 . |
| MLA | Lian, Sheng et al. "Fine-grained epicardial adipose tissue segmentation in cardiac CT images with position priors and edge enhancement" . | BIOMEDICAL ENGINEERING LETTERS (2025) . |
| APA | Lian, Sheng , Yuan, Qinghe , Su, Qiong , Liu, Jiayao , Chai, Dajun . Fine-grained epicardial adipose tissue segmentation in cardiac CT images with position priors and edge enhancement . | BIOMEDICAL ENGINEERING LETTERS , 2025 . |
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Object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: adaptive small object enhancement (ASOE) and dynamic class-balanced copy-paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, maintaining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a 37.5% average precision (AP) on the VisDrone dataset, surpassing its counterparts by at least 3.1%.
Keyword ：
class imbalance class imbalance object detection object detection scale variations scale variations UAV images UAV images
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| GB/T 7714 | Li, Zhenteng , Lian, Sheng , Pan, Dengfeng et al. AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes [J]. | REMOTE SENSING , 2025 , 17 (9) . |
| MLA | Li, Zhenteng et al. "AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes" . | REMOTE SENSING 17 . 9 (2025) . |
| APA | Li, Zhenteng , Lian, Sheng , Pan, Dengfeng , Wang, Youlin , Liu, Wei . AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes . | REMOTE SENSING , 2025 , 17 (9) . |
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Semi-supervised medical image segmentation (SS-MIS) has gained growing interest for its ability to mitigate costly annotation. However, existing solutions struggle in this field, primarily for neglecting challenging boundary regions and the similarity between target structures. These issues result in imprecise boundary segmentation and unreasonable predictions. To this end, this paper presents a novel SS-MIS framework, integrating Boundary-aware Multi-Task (BMT) strategy and Dynamic Competitive Contrastive Learning (DCCL). BMT employs a boundary-aware multi-task strategy to focus the model on boundary regions, and the extracted boundary features are further integrated with the segmentation features to achieve more precise predictions. Additionally, to promote compact distribution for identical classes in the feature space, DCCL adopts a dynamic competition strategy to generate more reliable feature prototypes. The model then performs contrastive learning by minimizing the distance between its features and the corresponding feature prototypes. This strategy further enhances the model's ability to discriminate between different classes. Extensive experiments on three public datasets, i.e., ACDC, PROMISE12, and BUSI, demonstrate that our method achieves promising results, particularly regarding boundary regions and class discrimination. Specifically, with only 10% labeled data, we achieved the Dice scores of 87.89%, 74.92%, and 65.68% on ACDC, PROMISE12, and BUSI, respectively. These results notably outperform the comparative CPS by 2.36%, 18.64%, and 5.48%, respectively. The code is publicly available at: https://github.com/linzk99/BMT-DCCL.
Keyword ：
Boundary-aware multi-task learning Boundary-aware multi-task learning Competitive contrastive learning Competitive contrastive learning Medical image segmentation Medical image segmentation Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Lin, Zhikang , Li, Zhenteng , Su, Jiawei et al. Enhancing semi-supervised medical image segmentation with boundary awareness and Contrastive [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 103 . |
| MLA | Lin, Zhikang et al. "Enhancing semi-supervised medical image segmentation with boundary awareness and Contrastive" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 103 (2025) . |
| APA | Lin, Zhikang , Li, Zhenteng , Su, Jiawei , Li, Lei , Lian, Sheng . Enhancing semi-supervised medical image segmentation with boundary awareness and Contrastive . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 103 . |
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Background Early prediction of heart failure (HF) after acute myocardial infarction (AMI) is essential for personalized treatment. We aimed to use interpretable machine learning (ML) methods to develop a risk prediction model for HF in AMI patients.Methods We retrospectively included patients initially with AMI who received percutaneous coronary intervention (PCI) in our hospital from November 2016 to February 2020. The primary endpoint was the occurrence of HF within 3 years after operation. For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. The performance evaluation of the prediction model was carried out on the training set and the testing set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), calibration plot, and decision curve analysis (DCA). In addition, we used the Shapley Additive Explanations (SHAP) value to determine the importance of the selected features and interpret the optimal model.Results A total of 1220 AMI patients were included and 244 (20%) patients developed HF during follow-up. Among the four evaluated ML models, the XGBoost model exhibited exceptional accuracy, with an AUC value of 0.922. The SHAP method showed that left ventricular ejection fraction (LVEF), left ventricular end-systolic diameter (LVDs) and lactate dehydrogenase (LDH) were identified as the three most important characteristics to predict HF risk in AMI patients. Individual risk assessment was performed using SHAP plots and waterfall plot analysis.Conclusions Our research demonstrates the potential of ML methods in the early prediction of HF risk in AMI patients. Furthermore, it enhances the interpretability of the XGBoost model through SHAP analysis to guide clinical decision-making.
Keyword ：
acute myocardial infarction acute myocardial infarction heart failure heart failure machine learning machine learning predict predict shapley additive explanations shapley additive explanations
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| GB/T 7714 | Lin, Qingqing , Zhao, Wenxiang , Zhang, Hailin et al. Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model [J]. | FRONTIERS IN CARDIOVASCULAR MEDICINE , 2025 , 12 . |
| MLA | Lin, Qingqing et al. "Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model" . | FRONTIERS IN CARDIOVASCULAR MEDICINE 12 (2025) . |
| APA | Lin, Qingqing , Zhao, Wenxiang , Zhang, Hailin , Chen, Wenhao , Lian, Sheng , Ruan, Qinyun et al. Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model . | FRONTIERS IN CARDIOVASCULAR MEDICINE , 2025 , 12 . |
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Semi-supervised learning has garnered significant interest as a method to alleviate the burden of data annotation. Recently, semi-supervised medical image segmentation has garnered significant interest that can alleviate the burden of densely annotated data. Substantial advancements have been achieved by integrating consistency-regularization and pseudo-labeling techniques. The quality of the pseudo-labels is crucial in this regard. Unreliable pseudo-labeling can result in the introduction of noise, leading the model to converge to suboptimal solutions. To address this issue, we propose learning from reliable pseudo-labels. In this paper, we tackle two critical questions in learning from reliable pseudo-labels: which pseudo-labels are reliable and how reliable are they? Specifically, we conduct a comparative analysis of two subnetworks to address both challenges. Initially, we compare the prediction confidence of the two subnetworks. A higher confidence score indicates a more reliable pseudo-label. Subsequently, we utilize intra-class similarity to assess the reliability of the pseudo-labels to address the second challenge. The greater the intra-class similarity of the predicted classes, the more reliable the pseudo-label. The subnetwork selectively incorporates knowledge imparted by the other subnetwork model, contingent on the reliability of the pseudo labels. By reducing the introduction of noise from unreliable pseudo-labels, we are able to improve the performance of segmentation. To demonstrate the superiority of our approach, we conducted an extensive set of experiments on three datasets: Left Atrium, Pancreas-CT and Brats-2019. The experimental results demonstrate that our approach achieves state-of-the-art performance. Code is available at: https://github.com/Jiawei0o0/mutual-learning-with-reliable-pseudo-labels.
Keyword ：
Intra-class similarity Intra-class similarity Medical image segmentation Medical image segmentation Pseudo-labels Pseudo-labels Semi-supervised learning Semi-supervised learning Uncertainty Uncertainty
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| GB/T 7714 | Su, Jiawei , Luo, Zhiming , Lian, Sheng et al. Mutual learning with reliable pseudo label for semi-supervised medical image segmentation [J]. | MEDICAL IMAGE ANALYSIS , 2024 , 94 . |
| MLA | Su, Jiawei et al. "Mutual learning with reliable pseudo label for semi-supervised medical image segmentation" . | MEDICAL IMAGE ANALYSIS 94 (2024) . |
| APA | Su, Jiawei , Luo, Zhiming , Lian, Sheng , Lin, Dazhen , Li, Shaozi . Mutual learning with reliable pseudo label for semi-supervised medical image segmentation . | MEDICAL IMAGE ANALYSIS , 2024 , 94 . |
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Different brain tumor magnetic resonance imaging (MRI) modalities provide diverse tumor-specific information. Previous works have enhanced brain tumor segmentation performance by integrating multiple MRI modalities. However, multi-modal MRI data are often unavailable in clinical practice. An incomplete modality leads to missing tumor-specific information, which degrades the performance of existing models. Various strategies have been proposed to transfer knowledge from a full modality network (teacher) to an incomplete modality one (student) to address this issue. However, they neglect the fact that brain tumor segmentation is a structural prediction problem that requires voxel semantic relations. In this paper, we propose a Reconstruct Incomplete Relation Network (RIRN) that transfers voxel semantic relational knowledge from the teacher to the student. Specifically, we propose two types of voxel relations to incorporate structural knowledge: Class-relative relations (CRR) and Class-agnostic relations (CAR). The CRR groups voxels into different tumor regions and constructs a relation between them. The CAR builds a global relation between all voxel features, complementing the local inter-region relation. Moreover, we use adversarial learning to align the holistic structural prediction between the teacher and the student. Extensive experimentation on both the BraTS 2018 and BraTS 2020 datasets establishes that our method outperforms all state-of-the-art approaches.
Keyword ：
Brain tumor segmentation Brain tumor segmentation Incomplete modalities Incomplete modalities Knowledge distillation Knowledge distillation Structural relation knowledge Structural relation knowledge
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| GB/T 7714 | Su, Jiawei , Luo, Zhiming , Wang, Chengji et al. Reconstruct incomplete relation for incomplete modality brain tumor segmentation [J]. | NEURAL NETWORKS , 2024 , 180 . |
| MLA | Su, Jiawei et al. "Reconstruct incomplete relation for incomplete modality brain tumor segmentation" . | NEURAL NETWORKS 180 (2024) . |
| APA | Su, Jiawei , Luo, Zhiming , Wang, Chengji , Lian, Sheng , Lin, Xuejuan , Li, Shaozi . Reconstruct incomplete relation for incomplete modality brain tumor segmentation . | NEURAL NETWORKS , 2024 , 180 . |
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Abdominal tumor segmentation is a crucial yet challenging step during the screening and diagnosis of tumors. While 3D segmentation models provide powerful performance, they demand substantial computational resources. Additionally, in 3D data, tumors often represent a small portion, leading to imbalanced data and potentially overlooking crucial information. Conversely, 2D segmentation models have a lightweight structure, but disregard the inter-slice correlation, risking the loss of tumor in edge slices. To address these challenges, this paper proposes a novel Position-Aware and Key Slice Feature Sharing 2D tumor segmentation model (PAKS-Net). Leveraging the Swin-Transformer, we effectively model the global features within each slice, facilitating essential information extraction. Furthermore, we introduce a Position-Aware module to capture the spatial relationship between tumors and their corresponding organs, mitigating noise and interference from surrounding organ tissues. To enhance the edge slice segmentation accuracy, we employ key slices to assist in the segmentation of other slices to prioritize tumor regions. Through extensive experiments on three abdominal tumor segmentation CT datasets and a lung tumor segmentation CT dataset, PAKS-Net demonstrates superior performance, reaching 0.893, 0.769, 0.598 and 0.738 tumor DSC on the KiTS19, LiTS17, pancreas and LOTUS datasets, surpassing 3D segmentation models, while remaining computationally efficient with fewer parameters. © 2024
Keyword ：
Computerized tomography Computerized tomography Deep learning Deep learning Diagnosis Diagnosis Tumors Tumors
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| GB/T 7714 | He, Jiezhou , Luo, Zhiming , Lian, Sheng et al. Towards accurate abdominal tumor segmentation: A 2D model with Position-Aware and Key Slice Feature Sharing [J]. | Computers in Biology and Medicine , 2024 , 179 . |
| MLA | He, Jiezhou et al. "Towards accurate abdominal tumor segmentation: A 2D model with Position-Aware and Key Slice Feature Sharing" . | Computers in Biology and Medicine 179 (2024) . |
| APA | He, Jiezhou , Luo, Zhiming , Lian, Sheng , Su, Songzhi , Li, Shaozi . Towards accurate abdominal tumor segmentation: A 2D model with Position-Aware and Key Slice Feature Sharing . | Computers in Biology and Medicine , 2024 , 179 . |
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Facioscapulohumeral muscular dystrophy type 1 (FSHD1) patients exhibit marked variability in both age at onset (AAO) and disease severity. Early onset FSHD1 patients are at an increased risk of severe weakness, and early onset has been tentatively linked to the length of D4Z4 repeat units (RUs) and methylation levels. The present study explored potential relationships among genetic characteristics, AAO and disease severity in FSHD1. This retrospective and observational cohort study was conducted at the Fujian Neuromedical Centre (FNMC) in China. Genetically confirmed participants with FSHD1 recruited from 2001 to 2023 underwent distal D4Z4 methylation assessment. Disease severity was assessed by FSHD clinical score, age-corrected clinical severity score (ACSS) and onset age of lower extremity involvement. Mediation analyses were used to explore relationships among genetic characteristics, AAO and disease severity. Finally, machine learning was employed to explore AAO prediction in FSHD1. A total of 874 participants (including 804 symptomatic patients and 70 asymptomatic carriers) were included. Multivariate Cox regression analyses indicated that male gender, low DUZ4 RUs, low CpG6 methylation levels, non-mosaic mutation and de novo mutation were independently associated with early onset in FSHD1. Early onset patients (AAO < 10 years) had both a significantly higher proportion and an earlier median onset age of lower extremity involvement compared to the typical adolescent onset (10 <= AAO < 20 years), typical adult onset (20 <= AAO < 30 years) and late onset (AAO >= 30 years) subgroups. AAO was negatively correlated with both clinical score and ACSS. We found that AAO exerted mediation effects, accounting for 12.2% of the total effect of D4Z4 RUs and CpG6 methylation levels on ACSS and 38.6% of the total effect of D4Z4 RUs and CpG6 methylation levels on onset age of lower extremity involvement. A random forest model that incorporated variables including gender, age at examination, inheritance pattern, mosaic mutation, D4Z4 RUs and D4Z4 methylation levels (at CpG3, CpG6 and CpG10 loci) performed well for AAO prediction. The predicted AAO (pAAO) was negatively correlated with ACSS (Spearman's rho = -0.692). Our study revealed independent contributions from D4Z4 RUs, D4Z4 methylation levels, mosaic mutation and inheritance pattern on AAO variation in FSHD1. AAO mediates effects of D4Z4 RUs and methylation levels on disease severity. The pAAO values from our random forest model informatively reflect disease severity, offering insights that can support efficacious patient management.
Keyword ：
age at onset (AAO) age at onset (AAO) facioscapulohumeral muscular dystrophy type 1 (FSHD1) facioscapulohumeral muscular dystrophy type 1 (FSHD1) mediation analysis mediation analysis prediction model prediction model
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| GB/T 7714 | Zheng, Fuze , Lin, Yawen , Qiu, Liangliang et al. Age at onset mediates genetic impact on disease severity in facioscapulohumeral muscular dystrophy [J]. | BRAIN , 2024 . |
| MLA | Zheng, Fuze et al. "Age at onset mediates genetic impact on disease severity in facioscapulohumeral muscular dystrophy" . | BRAIN (2024) . |
| APA | Zheng, Fuze , Lin, Yawen , Qiu, Liangliang , Zheng, Ying , Zeng, Minghui , Lin, Xiaodan et al. Age at onset mediates genetic impact on disease severity in facioscapulohumeral muscular dystrophy . | BRAIN , 2024 . |
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Semi-supervised learning has emerged as a critical approach for addressing medical image segmentation with limited annotation, and pseudo labeling-based methods made significant progress for this task. However, the varying quality of pseudo labels poses a challenge to model generalization. In this paper, we propose a Voxel-wise CLIP-enhanced model for semi-supervised medical image Segmentation (VCLIPSeg). Our model incorporates three modules: Voxel-Wise Prompts Module (VWPM), Vision-Text Consistency Module (VTCM), and Dynamic Labeling Branch (DLB). The VWPM integrates CLIP embeddings in a voxel-wise manner, learning the semantic relationships among pixels. The VTCM constrains the image prototype features, reducing the impact of noisy data. The DLB adaptively generates pseudo-labels, effectively leveraging the unlabeled data. Experimental results on the Left Atrial (LA) dataset and Pancreas-CT dataset demonstrate the superiority of our method over state-of-the-art approaches in terms of the Dice score. For instance, it achieves a Dice score of 88.51% using only 5% labeled data from the LA dataset.
Keyword ：
CLIP CLIP Organ segmentation Organ segmentation Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Li, Lei , Lian, Sheng , Luo, Zhiming et al. VCLIPSeg: Voxel-Wise CLIP-Enhanced Model for Semi-supervised Medical Image Segmentation [J]. | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT IX , 2024 , 15009 : 692-701 . |
| MLA | Li, Lei et al. "VCLIPSeg: Voxel-Wise CLIP-Enhanced Model for Semi-supervised Medical Image Segmentation" . | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT IX 15009 (2024) : 692-701 . |
| APA | Li, Lei , Lian, Sheng , Luo, Zhiming , Wang, Beizhan , Li, Shaozi . VCLIPSeg: Voxel-Wise CLIP-Enhanced Model for Semi-supervised Medical Image Segmentation . | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT IX , 2024 , 15009 , 692-701 . |
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| GB/T 7714 | Lian, Sheng , Luo, Zhiming . Cutting-Edge Machine Learning in Biomedical Image Analysis: Editorial for Bioengineering Special Issue: "Recent Advance of Machine Learning in Biomedical Image Analysis" [J]. | BIOENGINEERING-BASEL , 2024 , 11 (11) . |
| MLA | Lian, Sheng et al. "Cutting-Edge Machine Learning in Biomedical Image Analysis: Editorial for Bioengineering Special Issue: "Recent Advance of Machine Learning in Biomedical Image Analysis"" . | BIOENGINEERING-BASEL 11 . 11 (2024) . |
| APA | Lian, Sheng , Luo, Zhiming . Cutting-Edge Machine Learning in Biomedical Image Analysis: Editorial for Bioengineering Special Issue: "Recent Advance of Machine Learning in Biomedical Image Analysis" . | BIOENGINEERING-BASEL , 2024 , 11 (11) . |
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