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学者姓名：杨明静
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Abstract ：
Myocardial pathology segmentation (MyoPS) is essential for the diagnosis, treatment, and prognosis of myocardial infarction (MI). Recent deep learning MyoPS models have shown promising performance on independent identically distributed data. However, due to the domain shift caused by multi-center data, many methods often fail to generalize to unseen data. In this work, we propose a novel domain generalization framework (SMCANet) for MyoPS, which leverages data statistics (mean and standard deviation) modeling (DSM) and feature covariance alignment (FCA) during training to learn domain-invariant information. Specifically, DSM first models the statistical information of the data images and then performs perturbation-based random sampling to enhance the diversity of data representations. FCA reduces feature discrepancies across different distributions while preserving domain content information through the use of covariance matching loss (CML) and cross-covariance loss (CCL). Experiments on cross-domain MyoPS datasets from CARE2024 MyoPS++ challenge demonstrate that our framework can achieve promising generalization performance without altering the network architecture or employing complex data augmentation networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keyword ：
COVID-19 COVID-19
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| GB/T 7714 | Dong, Jinwei , Wan, Kaiwen , Lin, Bogen et al. Domain Generalized Myocardial Pathology Segmentation via Data Statistics Modeling and Feature Covariance Alignment [C] . 2025 : 46-54 . |
| MLA | Dong, Jinwei et al. "Domain Generalized Myocardial Pathology Segmentation via Data Statistics Modeling and Feature Covariance Alignment" . (2025) : 46-54 . |
| APA | Dong, Jinwei , Wan, Kaiwen , Lin, Bogen , Yang, Mingjing . Domain Generalized Myocardial Pathology Segmentation via Data Statistics Modeling and Feature Covariance Alignment . (2025) : 46-54 . |
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Cardiovascular diseases are a leading cause of mortality worldwide, necessitating accurate segmentation of cardiac structures from 3D CT and MR images for effective diagnosis and treatment planning. Manual segmentation is time-consuming, labor-intensive, and challenging due to the complexity of cardiac anatomy structures. General CNNs, while effective, often struggle to generalize across different imaging domains. To address these issues, we developed a PPM-UNet, which integrates a probabilistic perturbation module (PPM) into the traditional U-Net architecture. Here, the PPM is introduced to control randomness during training, enhancing the model’s robustness and improving its performance across different imaging domains. Furthermore, the PPM is lightweight and easy to implement, making it a practical addition to existing network architectures for domain generalization tasks in medical imaging. Evaluated on the WHS++ dataset, our method demonstrates superior performance in segmenting cardiac structures, particularly in unseen domains, as evidenced by improved Dice Similarity Coefficient, Precision, Sensitivity, and Hausdorff Distance metrics. In addition to its performance benefits, the proposed module requires minimal computational overhead, making it practical for real-world clinical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keyword ：
Cardiology Cardiology Computerized tomography Computerized tomography Diffusion tensor imaging Diffusion tensor imaging Image segmentation Image segmentation Intelligent systems Intelligent systems Magnetic resonance imaging Magnetic resonance imaging
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| GB/T 7714 | Lin, Bogen , Dong, Jinwei , Zheng, Yaqiong et al. Enhancing Domain Generalization for Cardiac Image Segmentation with Probabilistic Perturbation [C] . 2025 : 1-12 . |
| MLA | Lin, Bogen et al. "Enhancing Domain Generalization for Cardiac Image Segmentation with Probabilistic Perturbation" . (2025) : 1-12 . |
| APA | Lin, Bogen , Dong, Jinwei , Zheng, Yaqiong , Xiang, Yihan , Yang, Mingjing . Enhancing Domain Generalization for Cardiac Image Segmentation with Probabilistic Perturbation . (2025) : 1-12 . |
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Accurate segmentation of myocardial pathology, including scar and edema, from magnetic resonance images (MRI) is of significant clinical importance for diagnosing, planning treatment, and assessing prognosis in cardiovascular diseases. Multi-sequence cardiac magnetic resonance images provide additional information for pathological segmentation, while ventricular blood pool regions offer positional anatomical priors for identifying scar and edema. Therefore, we propose a multi-task framework based on SimAM-UNet, where SimAM generates feature weights to better focus on modality-specific features for the segmentation of myocardial pathology using multi-sequence cardiac magnetic resonance images. The first task involves segmenting and extracting features of the myocardium and blood pool, which are relatively easy to obtain. The second task focuses on segmenting scars and edema by integrating positional feature constraints derived from the myocardium and blood pool. To address the multi-center data problem encountered in real-world data, we incorporate a data augmentation pipeline that simulates the generation of data samples from different centers, enhancing the domain generalization of our method. Our proposed method was evaluated on the MyoPS++ track of the CARE2024 Challenge, and the experimental results demonstrated the effectiveness of our multi-task framework for myocardial pathological segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keyword ：
Bioinformatics Bioinformatics Cardiology Cardiology Diagnosis Diagnosis Image segmentation Image segmentation Nuclear magnetic resonance Nuclear magnetic resonance Pathology Pathology
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| GB/T 7714 | Wu, Yilin , Yang, Xiao , Liu, Yongli et al. Multi-task Framework for Myocardial Pathological Segmentation on Multi-sequence Cardiac Magnetic Resonance [C] . 2025 : 116-125 . |
| MLA | Wu, Yilin et al. "Multi-task Framework for Myocardial Pathological Segmentation on Multi-sequence Cardiac Magnetic Resonance" . (2025) : 116-125 . |
| APA | Wu, Yilin , Yang, Xiao , Liu, Yongli , Zheng, Jiahao , Yang, Mingjing . Multi-task Framework for Myocardial Pathological Segmentation on Multi-sequence Cardiac Magnetic Resonance . (2025) : 116-125 . |
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Myocardial infarction (MI) is a leading cause of death and disability, underscoring the critical importance of accurate and effective assessment of myocardial viability. However, existing CNNs are insufficient for extracting long-range contexts information, while the computational cost of Transformer architectures is prohibitively high, making segmentation of scars and edema in multi-center cardiac MRI (CMR) sequences particularly challenging. To address this issue, we propose Mamaba Enhanced UNet (ME-UNet), a deep learning architecture that integrates the advantages of the U-Net model with residual mechanisms and state space models (SSMs) for the segmentation of myocardial scars and edema. The advantage of ME-UNet lies in its Vision Mamba block, which significantly enhances the model’s ability to extract global information. Additionally, Enhanced 3D residual block (E3DR) further enhances and consolidates the extraction of local and spatial information. Experimental results demonstrate that ME-UNet exhibits exceptional performance on the MyoPS challenge dataset, effectively segmenting myocardial scars and edema, thereby validating the efficacy of our framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keyword ：
Cardiography Cardiography Deep learning Deep learning Image enhancement Image enhancement Image segmentation Image segmentation Magnetic resonance imaging Magnetic resonance imaging Medical imaging Medical imaging State space methods State space methods
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| GB/T 7714 | Zhu, Zhanpeng , Lin, Yuqi , Yang, Mingjing . ME-UNet: Enhancing Mamba for Myocardial Pathology Segmentation in Multi-center Multi-sequence CMR Images [C] . 2025 : 66-76 . |
| MLA | Zhu, Zhanpeng et al. "ME-UNet: Enhancing Mamba for Myocardial Pathology Segmentation in Multi-center Multi-sequence CMR Images" . (2025) : 66-76 . |
| APA | Zhu, Zhanpeng , Lin, Yuqi , Yang, Mingjing . ME-UNet: Enhancing Mamba for Myocardial Pathology Segmentation in Multi-center Multi-sequence CMR Images . (2025) : 66-76 . |
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Pedestrian Attribute Recognition (PAR) involves identifying the attributes of individuals in person images. Existing PAR methods typically rely on CNNs as the backbone network to extract pedestrian features. However, CNNs process only one adjacent region at a time, leading to the loss of long-range inter-relations between different attribute-specific regions. To address this limitation, we leverage the Vision Transformer (ViT) instead of CNNs as the backbone for PAR, aiming to model long-range relations and extract more robust features. However, PAR suffers from an inherent attribute imbalance issue, causing ViT to naturally focus more on attributes that appear frequently in the training set and ignore some pedestrian attributes that appear less. The native features extracted by ViT are not able to tolerate the imbalance attribute distribution issue. To tackle this issue, we propose two novel components: the Selective Feature Activation Method (SFAM) and the Orthogonal Feature Activation Loss. SFAM smartly suppresses the more informative attribute-specific features, compelling the PAR model to capture discriminative features from regions that are easily overlooked. The proposed loss enforces an orthogonal constraint on the original feature extracted by ViT and the suppressed features from SFAM, promoting the complementarity of features in space. We conduct experiments on several benchmark PAR datasets, including PETA, PA100K, RAPv1, and RAPv2, demonstrating the effectiveness of our method. Specifically, our method outperforms existing state-of-the-art approaches by GRL, IAA-Caps, ALM, and SSC in terms of mA on the four datasets, respectively. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Artificial intelligence Artificial intelligence Chemical activation Chemical activation
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| GB/T 7714 | Wu, Junyi , Huang, Yan , Gao, Min et al. Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition [C] . 2024 : 6039-6047 . |
| MLA | Wu, Junyi et al. "Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition" . (2024) : 6039-6047 . |
| APA | Wu, Junyi , Huang, Yan , Gao, Min , Niu, Yuzhen , Yang, Mingjing , Gao, Zhipeng et al. Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition . (2024) : 6039-6047 . |
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Unsupervised domain adaptation(UDA) aims to mitigate the performance drop of models tested on the target domain, due to the domain shift from the target to sources. Most UDA segmentation methods focus on the scenario of solely single source domain. However, in practical situations data with gold standard could be available from multiple sources (domains), and the multi-source training data could provide more information for knowledge transfer. How to utilize them to achieve better domain adaptation yet remains to be further explored. This work investigates multi-source UDA and proposes a new framework for medical image segmentation. Firstly, we employ a multi-level adversarial learning scheme to adapt features at different levels between each of the source domains and the target, to improve the segmentation performance. Then, we propose a multi-model consistency loss to transfer the learned multi-source knowledge to the target domain simultaneously. Finally, we validated the proposed framework on two applications, i.e., multi-modality cardiac segmentation and cross-modality liver segmentation. The results showed our method delivered promising performance and compared favorably to state-of-the-art approaches.
Keyword ：
Domain adaptation Domain adaptation medical image segmentation medical image segmentation multi-source multi-source unsupervised learning unsupervised learning
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| GB/T 7714 | Pei, Chenhao , Wu, Fuping , Yang, Mingjing et al. Multi-Source Domain Adaptation for Medical Image Segmentation [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2024 , 43 (4) : 1640-1651 . |
| MLA | Pei, Chenhao et al. "Multi-Source Domain Adaptation for Medical Image Segmentation" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 43 . 4 (2024) : 1640-1651 . |
| APA | Pei, Chenhao , Wu, Fuping , Yang, Mingjing , Pan, Lin , Ding, Wangbin , Dong, Jinwei et al. Multi-Source Domain Adaptation for Medical Image Segmentation . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2024 , 43 (4) , 1640-1651 . |
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Abstract ：
Pedestrian Attribute Recognition (PAR) involves identifying the attributes of individuals in person images. Existing PAR methods typically rely on CNNs as the backbone network to extract pedestrian features. However, CNNs process only one adjacent region at a time, leading to the loss of long-range inter-relations between different attribute-specific regions. To address this limitation, we leverage the Vision Transformer (ViT) instead of CNNs as the backbone for PAR, aiming to model long-range relations and extract more robust features. However, PAR suffers from an inherent attribute imbalance issue, causing ViT to naturally focus more on attributes that appear frequently in the training set and ignore some pedestrian attributes that appear less. The native features extracted by ViT are not able to tolerate the imbalance attribute distribution issue. To tackle this issue, we propose two novel components: the Selective Feature Activation Method (SFAM) and the Orthogonal Feature Activation Loss. SFAM smartly suppresses the more informative attribute-specific features, compelling the PAR model to capture discriminative features from regions that are easily overlooked. The proposed loss enforces an orthogonal constraint on the original feature extracted by ViT and the suppressed features from SFAM, promoting the complementarity of features in space. We conduct experiments on several benchmark PAR datasets, including PETA, PA100K, RAPv1, and RAPv2, demonstrating the effectiveness of our method. Specifically, our method outperforms existing state-of-the-art approaches by GRL, IAACaps, ALM, and SSC in terms of mA on the four datasets, respectively.
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| GB/T 7714 | Wu, Junyi , Huang, Yan , Gao, Min et al. Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition [J]. | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 6 , 2024 : 6039-6047 . |
| MLA | Wu, Junyi et al. "Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition" . | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 6 (2024) : 6039-6047 . |
| APA | Wu, Junyi , Huang, Yan , Gao, Min , Niu, Yuzhen , Yang, Mingjing , Gao, Zhipeng et al. Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition . | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 6 , 2024 , 6039-6047 . |
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Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods have emerged as a pivotal strategy for cross modality analysis. These methods typically utilize generative adversarial networks (GANs) for both image-level and feature-level domain adaptation through the transformation and reconstruction of images, assuming the features between domains are well-aligned. However, this assumption falters with significant gaps between different medical image modalities, such as MRI and CT. These gaps hinder the effective training of segmentation networks with cross-modality images and can lead to misleading training guidance and instability. To address these challenges, this paper introduces a novel approach comprising a cross-modality feature alignment sub-network and a cross pseudo supervised dual-stream segmentation sub-network. These components work together to bridge domain discrepancies more effectively and ensure a stable training environment. The feature alignment sub-network is designed for the bidirectional alignment of features between the source and target domains, incorporating a self-attention module to aid in learning structurally consistent and relevant information. The segmentation sub-network leverages an enhanced cross-pseudo-supervised loss to harmonize the output of the two segmentation networks, assessing pseudo-distances between domains to improve the pseudo-label quality and thus enhancing the overall learning efficiency of the framework. This method's success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks.
Keyword ：
cross modality segmentation cross modality segmentation cross pseudo supervision cross pseudo supervision feature alignment feature alignment unsupervised domain adaptation unsupervised domain adaptation
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| GB/T 7714 | Yang, Mingjing , Wu, Zhicheng , Zheng, Hanyu et al. Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning [J]. | DIAGNOSTICS , 2024 , 14 (16) . |
| MLA | Yang, Mingjing et al. "Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning" . | DIAGNOSTICS 14 . 16 (2024) . |
| APA | Yang, Mingjing , Wu, Zhicheng , Zheng, Hanyu , Huang, Liqin , Ding, Wangbin , Pan, Lin et al. Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning . | DIAGNOSTICS , 2024 , 14 (16) . |
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Purpose: The identification of early-stage Parkinson's disease (PD) is important for the effective management of patients, affecting their treatment and prognosis. Recently, structural brain networks (SBNs) have been used to diagnose PD. However, how to mine abnormal patterns from high-dimensional SBNs has been a challenge due to the complex topology of the brain. Meanwhile, the existing prediction mechanisms of deep learning models are often complicated, and it is difficult to extract effective interpretations. In addition, most works only focus on the classification of imaging and ignore clinical scores in practical applications, which limits the ability of the model. Inspired by the regional modularity of SBNs, we adopted graph learning from the perspective of node clustering to construct an interpretable framework for PD classification.Methods: In this study, a multi-task graph structure learning framework based on node clustering (MNC-Net) is proposed for the early diagnosis of PD. Specifically, we modeled complex SBNs into modular graphs that facilitated the representation learning of abnormal patterns. Traditional graph neural networks are optimized through graph structure learning based on node clustering, which identifies potentially abnormal brain regions and reduces the impact of irrelevant noise. Furthermore, we employed a regression task to link clinical scores to disease classification, and incorporated latent domain information into model training through multi-task learning.Results: We validated the proposed approach on the Parkinsons Progression Markers Initiative dataset. Exper-imental results showed that our MNC-Net effectively separated the early-stage PD from healthy controls(HC) with an accuracy of 95.5%. The t-SNE figures have showed that our graph structure learning method can capture more efficient and discriminatory features. Furthermore, node clustering parameters were used as important weights to extract salient task-related brain regions(ROIs). These ROIs are involved in the development of mood disorders, tremors, imbalances and other symptoms, highlighting the importance of memory, language and mild motor function in early PD. In addition, statistical results from clinical scores confirmed that our model could capture abnormal connectivity that was significantly different between PD and HC. These results are consistent with previous studies, demonstrating the interpretability of our methods.
Keyword ：
Clinical scores Clinical scores Early Parkinson?s disease Early Parkinson?s disease Graph neural networks Graph neural networks Structural brain network Structural brain network
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| GB/T 7714 | Huang, Liqin , Ye, Xiaofang , Yang, Mingjing et al. MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 152 . |
| MLA | Huang, Liqin et al. "MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis" . | COMPUTERS IN BIOLOGY AND MEDICINE 152 (2023) . |
| APA | Huang, Liqin , Ye, Xiaofang , Yang, Mingjing , Pan, Lin , Zheng, Shao hua . MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis . | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 152 . |
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Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were 0.614 +/- 0.231 and 0.644 +/- 0.153 for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).
Keyword ：
Benchmark Benchmark Cardiac magnetic resonance Cardiac magnetic resonance Multi-sequence MRI Multi-sequence MRI Multi-source images Multi-source images Myocardial pathology segmentation Myocardial pathology segmentation
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| GB/T 7714 | Li, Lei , Wu, Fuping , Wang, Sihan et al. MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images [J]. | MEDICAL IMAGE ANALYSIS , 2023 , 87 . |
| MLA | Li, Lei et al. "MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images" . | MEDICAL IMAGE ANALYSIS 87 (2023) . |
| APA | Li, Lei , Wu, Fuping , Wang, Sihan , Luo, Xinzhe , Martin-Isla, Carlos , Zhai, Shuwei et al. MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images . | MEDICAL IMAGE ANALYSIS , 2023 , 87 . |
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