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学者姓名：杨明静
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Abstract ：
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. © 2023 IEEE.
Keyword ：
Job analysis Job analysis Knowledge management Knowledge management Medical imaging Medical imaging Semantics Semantics Semantic Segmentation Semantic Segmentation
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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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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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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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Accurate and efficient segmentation of multiple abdominal organs from medical images is crucial for clinical applications such as disease diagnosis and treatment planning. In this paper, we propose a novel approach for abdominal organ segmentation using the U-Net architecture. Our method addresses the challenges posed by anatomical variations and the proximity of organs in the abdominal region. To improve the segmentation accuracy, we introduce an attention mechanism into the U-Net architecture. This mechanism allows the network to focus on salient regions and suppress irrelevant background regions, enhancing the overall segmentation performance. Additionally, we incorporate 3D information by connecting three consecutive slices as 3-dimensional inputs. This enables us to exploit the spatial context across the slices while minimizing the increase in GPU memory usage. We evaluate our proposed method on the MICCAI FLARE 2023 validation dataset, the mean DSC is 0.3683 and the mean NSD is 0.3668. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Keyword ：
attention mechanism attention mechanism organ segmentation organ segmentation U-Net U-Net
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| GB/T 7714 | Lei, R. , Yang, M. . 2.5D U-Net for Abdominal Multi-organ Segmentation [未知]. |
| MLA | Lei, R. et al. "2.5D U-Net for Abdominal Multi-organ Segmentation" [未知]. |
| APA | Lei, R. , Yang, M. . 2.5D U-Net for Abdominal Multi-organ Segmentation [未知]. |
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Abdominal organ segmentation can help doctors to have a more intuitive observation of the abdominal organ structure and tissue lesion structure, thereby improving the accuracy of disease diagnosis. Accurate segmentation results can provide valuable information for clinical diagnosis and follow-up, such as organ size, location, boundary status, and spatial relationship of multiple organs. Manual labels are precious and difficult to obtain in medical segmentation, so the use of pseudo-labels is an irresistible trend. In this paper, we demonstrate that pseudo-labels are beneficial to enrich the learning samples and enhance the feature learning ability of the model for abdominal organs and tumors. In this paper, we propose a semi-supervised parallel segmentation model that simultaneously aggregates local and global information using parallel modules of CNNS and transformers at high scales. The two-stage strategy and lightweight network make our model extremely efficient. Our method achieved an average DSC score of 89.75% and 3.78% for the organs and tumors, respectively, on the testing set. The average NSD scores were 93.51% and 1.82% for the organs and tumors, respectively. The average running time and area under GPU memory-time curve are 14.85 s and 15963 MB. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Keyword ：
Abdominal organ and tumor segmentation Abdominal organ and tumor segmentation Hybrid architecture Hybrid architecture Pseudo-label Pseudo-label
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| GB/T 7714 | Chen, Y. , Wu, Z. , Chen, H. et al. Conformer: A Parallel Segmentation Network Combining Swin Transformer and Convolutional Neutral Network [未知]. |
| MLA | Chen, Y. et al. "Conformer: A Parallel Segmentation Network Combining Swin Transformer and Convolutional Neutral Network" [未知]. |
| APA | Chen, Y. , Wu, Z. , Chen, H. , Yang, M. . Conformer: A Parallel Segmentation Network Combining Swin Transformer and Convolutional Neutral Network [未知]. |
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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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Background: Automatic pulmonary artery-vein separation has considerable importance in the diagnosis and treatment of lung diseases. However, insufficient connectivity and spatial inconsistency have always been the problems of artery-vein separation. Methods: A novel automatic method for artery-vein separation in CT images is presented in this work. Specifically, a multi-scale information aggregated network (MSIA-Net) including multi-scale fusion blocks and deep supervision, is proposed to learn the features of artery-vein and aggregate additional semantic information, respectively. The proposed method integrates nine MSIA-Net models for artery-vein separation, vessel segmentation, and centerline separation tasks along with axial, coronal, and sagittal multi-view slices. First, the preliminary artery-vein separation results are obtained by the proposed multi-view fusion strategy (MVFS). Then, centerline correction algorithm (CCA) is used to correct the preliminary results of artery- vein separation by the centerline separation results. Finally, the vessel segmentation results are utilized to reconstruct the artery-vein morphology. In addition, weighted cross-entropy and dice loss are employed to solve the class imbalance problem. Results: We constructed 50 manually labeled contrast-enhanced computed CT scans for five-fold cross -validation, and experimental results demonstrated that our method achieves superior segmentation perfor-mance of 97.7%, 85.1%, and 84.9% on ACC, Pre, and DSC, respectively. Additionally, a series of ablation studies demonstrate the effectiveness of the proposed components. Conclusion: The proposed method can effectively solve the problem of insufficient vascular connectivity and correct the spatial inconsistency of artery-vein.
Keyword ：
Centerline correction Centerline correction CT images CT images Multi-scale information aggregated Multi-scale information aggregated Pulmonary artery-vein separation Pulmonary artery-vein separation
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| GB/T 7714 | Pan, Lin , Li, Zhaopei , Shen, Zhiqiang et al. Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 155 . |
| MLA | Pan, Lin et al. "Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation" . | COMPUTERS IN BIOLOGY AND MEDICINE 155 (2023) . |
| APA | Pan, Lin , Li, Zhaopei , Shen, Zhiqiang , Liu, Zheng , Huang, Liqin , Yang, Mingjing et al. Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation . | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 155 . |
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Parkinson's disease (PD) is a serious neurological disease. Many studies have preseted regions of interest such as substantia nigra (SN) for PD detection from magnetic resonance imaging (MRI). However, the SN is not the only region with remarkable tissue changes in PD MRIs. Patients with Prodromal Parkinson's Disease usually present with non-motor symptoms, and the associated brain regions may show varying degrees of damage on imaging. Therefore, exploring PD-related regions from whole-brain MRI is essential. In this study, we proposed an interpretable PD detection framework, including PD classification and feature region visualization. Specifically, we constructed a 3D ResNet model that could detect PD from whole-brain MRIs and discover other brain regions related to PD through 3D Gradient-weighted Class Activation Mapping (Grad-CAM) and Unified Parkinson's Disease Rating Scale (UPDRS). We obtained T1-Weighted MRIs from the Parkinson's Progression Markers Initiative (PPMI) database. The average classification accuracy of the 5-fold cross-validation and held-out dataset reached 96.1% and 94.5%, respectively. In addition, we used the 3D Grad-CAM framework to extract the weight of the feature map and obtain visual interpretation. The heat map highlighted the regions that were crucial for PD classification and found significant differences between PD and HC in frontal lobe related to linguistic semantic disorders. The UPDRS scores of PD and HC on the linguistic semantic function items were also remarkably different. Combined with previous studies, this work verified the significance of the frontal lobe and proved that the correlation between the frontal lobe and the pathogenesis of PD was explanatory.
Keyword ：
3D ResNet 3D ResNet Frontal lobe Frontal lobe Grad-CAM Grad-CAM MRI MRI Parkinson's diseases Parkinson's diseases Semantics Semantics
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| GB/T 7714 | Yang, Mingjing , Huang, Xianbin , Huang, Liqin et al. Diagnosis of Parkinson's disease based on 3D ResNet: The frontal lobe is crucial [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 85 . |
| MLA | Yang, Mingjing et al. "Diagnosis of Parkinson's disease based on 3D ResNet: The frontal lobe is crucial" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 85 (2023) . |
| APA | Yang, Mingjing , Huang, Xianbin , Huang, Liqin , Cai, Guoen . Diagnosis of Parkinson's disease based on 3D ResNet: The frontal lobe is crucial . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 85 . |
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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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