Query：
所有字段：(空)
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Atrial fibrillation (AF) is a prevalent heart rate arrhythmia and its incidence is increasing with the aging population. The late gadoliniumenhanced magnetic resonance imaging (LGE-MRI) provides pathologic changes in the left atrium, allowing for a detailed assessment of the left atrial anatomy, which is critical for diagnosis and treatment decisions in AF. The segmentation performance of current left atrial segmentation methods is significantly degraded when applied to multicenter data. In this work, we propose ResCAUNet, a deep learning method based on residual neural networks. Specifically, a pre-trained model driven residual segmentation network is first designed to alleviate the problem of gradient disappearance and help to extract more complex image features. Secondly, an adaptive scale weight loss function was introduced to solve the problem of discontinuous segmentation boundary, so as to ensure higher accuracy of object segmentation. Furthermore, the coordinate attention(CA) mechanism is introduced for adaptive weight allocation, focusing on the key parts of the image to improve the accuracy of left atrial reconstruction. Our method is evaluated on the LAScarQS2024 validation set and achieves an average Dice of 0.853. The evaluation results show that the proposed method is effective in left atrium segmentation of LGE-MRI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keyword ：
Deep neural networks Deep neural networks Diagnosis Diagnosis Diseases Diseases Image enhancement Image enhancement Image reconstruction Image reconstruction Image segmentation Image segmentation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Xinru , Gao, Ruikun , Zheng, Yuxin et al. A Left Atrial Automatic Segmentation Based on ResCAUNet [C] . 2025 : 139-148 . |
| MLA | Li, Xinru et al. "A Left Atrial Automatic Segmentation Based on ResCAUNet" . (2025) : 139-148 . |
| APA | Li, Xinru , Gao, Ruikun , Zheng, Yuxin , Zheng, Shaohua , Chen, Weisheng . A Left Atrial Automatic Segmentation Based on ResCAUNet . (2025) : 139-148 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Cardiac scarring and edema are critical pathological features of heart diseases. Accurate segmentation of these features in Cardiac Magnetic Resonance (CMR) imaging is crucial for understanding the pathological changes associated with cardiac diseases. In the field of myocardial scar and edema segmentation, it is of significant importance to study the C0, T2, and LGE modalities. These modalities offer different perspectives on myocardial tissue characteristics, aiding in the more accurate diagnosis and assessment of cardiac diseases. However, the high-intensity features of scars and edema cannot be directly obtained from individual CMR imaging sequences, making simultaneous accurate segmentation challenging. To address this, we propose a multi-modal, multi-channel fusion interactive progressive segmentation strategy that leverages the distinctive properties of each modality and the surrounding tissue characteristics for the segmentation of myocardial scars and edema. We have designed a multi-channel fusion interactive progressive segmentation model, suitable for scar and myocardial segmentation, which incorporates an attention mechanism that enhances channel information interaction within a U-Net structure to extract features across different modalities. On the MyoPS++ 2024 public dataset, our method achieved an average Dice score of 0.5486 for scar segmentation and 0.6081 for the segmentation of both scars and edema. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keyword ：
Cardiology Cardiology Diagnosis Diagnosis Diseases Diseases Image segmentation Image segmentation Magnetic resonance imaging Magnetic resonance imaging Pathology Pathology
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wang, Jingyan , Gong, Xiaojuan , Jin, Tangruoyi et al. Progressive Multi-channel Fusion Network for Myocardial Pathology Segmentation on Multi-modality CMR Images [C] . 2025 : 192-201 . |
| MLA | Wang, Jingyan et al. "Progressive Multi-channel Fusion Network for Myocardial Pathology Segmentation on Multi-modality CMR Images" . (2025) : 192-201 . |
| APA | Wang, Jingyan , Gong, Xiaojuan , Jin, Tangruoyi , Gao, Ruikun , Zheng, Shaohua , Chen, Weisheng . Progressive Multi-channel Fusion Network for Myocardial Pathology Segmentation on Multi-modality CMR Images . (2025) : 192-201 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
针对现有均匀量化的连续消除列表(Successive Cancellation List,SCL)译码算法中存储资源消耗大、布线延迟高的问题,提出了一种采用 5 bit非均匀量化方案的SCL译码算法.该算法保留均匀量化中的对数似然比(Log-Like-lihood Ratio,LLR)迭代计算方法,采用5 bit非均匀量化LLR,在LLR计算模块中设计查找表(Look-Up-Table,LUT)转为6 bit均匀量化LLR用于计算.仿真结果表明,提出的 5 bit非均匀量化SCL译码相比于 6 bit均匀量化 SCL译码器,在码率R=1/2、列表宽度L=2 和L=4 时,误帧率(Frame Erasure Rate,FER)性能损失在0.1dB以内.在硬件资源消耗方面,与 6 bit均匀量化译码器相比,5 bit非均匀量化方案译码器在 L=2 时触发器(Flip-Flop,FF)和块随机存取存储器(Block Random Access Memory,BRAM)存储资源消耗分别减少了 10.9%和 22%,吞吐量增加了 24%;L=4 时 FF和BRAM分别减少了 10%和 18.1%,吞吐量增加了 17.5%.
Keyword ：
极化码 极化码 现场可编程逻辑门阵列 现场可编程逻辑门阵列 连续消除列表译码 连续消除列表译码 非均匀量化 非均匀量化
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 魏少圣 , 熊启金 , 郑绍华 et al. 基于非均匀量化的极化码SCL译码器FPGA实现 [J]. | 无线电通信技术 , 2024 , 50 (6) : 1200-1208 . |
| MLA | 魏少圣 et al. "基于非均匀量化的极化码SCL译码器FPGA实现" . | 无线电通信技术 50 . 6 (2024) : 1200-1208 . |
| APA | 魏少圣 , 熊启金 , 郑绍华 , 陈平平 . 基于非均匀量化的极化码SCL译码器FPGA实现 . | 无线电通信技术 , 2024 , 50 (6) , 1200-1208 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Background: With the wide application of CT scanning, the separation of pulmonary arteries and veins (A/V) based on CT images plays an important role for assisting surgeons in preoperative planning of lung cancer surgery. However, distinguishing between arteries and veins in chest CT images remains challenging due to the complex structure and the presence of their similarities. Methods: We proposed a novel method for automatically separating pulmonary arteries and veins based on vessel topology information and a twin-pipe deep learning network. First, vessel tree topology is constructed by combining scale-space particles and multi-stencils fast marching (MSFM) methods to ensure the continuity and authenticity of the topology. Second, a twin-pipe network is designed to learn the multiscale differences between arteries and veins and the characteristics of the small arteries that closely accompany bronchi. Finally, we designed a topology optimizer that considers interbranch and intrabranch topological relationships to optimize the results of arteries and veins classification. Results: The proposed approach is validated on the public dataset CARVE14 and our private dataset. Compared with ground truth, the proposed method achieves an average accuracy of 90.1% on the CARVE14 dataset, and 96.2% on our local dataset. Conclusions: The method can effectively separate pulmonary arteries and veins and has good generalization for chest CT images from different devices, as well as enhanced and noncontrast CT image sequences from the same device.
Keyword ：
Chest CT images Chest CT images Preoperative planning Preoperative planning Pulmonary artery-vein segmentation Pulmonary artery-vein segmentation Topology reconstruction Topology reconstruction Twin-pipe network Twin-pipe network
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Lin , Yan, Xiaochao , Zheng, Yaoyong et al. Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction [J]. | PEERJ COMPUTER SCIENCE , 2023 , 9 . |
| MLA | Pan, Lin et al. "Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction" . | PEERJ COMPUTER SCIENCE 9 (2023) . |
| APA | Pan, Lin , Yan, Xiaochao , Zheng, Yaoyong , Huang, Liqin , Zhang, Zhen , Fu, Rongda et al. Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction . | PEERJ COMPUTER SCIENCE , 2023 , 9 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Objectives Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC.Methods We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC).Results In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features.Conclusion The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model.
Keyword ：
Deep learning Deep learning Obstructive colorectal cancer Obstructive colorectal cancer Peritumoral region Peritumoral region Radiomics Radiomics ResNet ResNet
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Lin , He, Tian , Huang, Zihan et al. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image [J]. | ABDOMINAL RADIOLOGY , 2023 , 48 (4) : 1246-1259 . |
| MLA | Pan, Lin et al. "Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image" . | ABDOMINAL RADIOLOGY 48 . 4 (2023) : 1246-1259 . |
| APA | Pan, Lin , He, Tian , Huang, Zihan , Chen, Shuai , Zhang, Junrong , Zheng, Shaohua et al. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image . | ABDOMINAL RADIOLOGY , 2023 , 48 (4) , 1246-1259 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Background and Objectives: Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree. Methods: Our framework segments the overall airway and small branches via the multi-information fusion convolution neural network (Mif-CNN) and the CNN-based region growing, respectively. In Mif-CNN, atrous spatial pyramid pooling (ASPP) is integrated into a u-shaped network, and it can expend the receptive field and capture multi-scale information. Meanwhile, boundary and location information are incorporated into semantic information. These information are fused to help Mif-CNN utilize additional context knowledge and useful features. To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches. A voxel classification network (VCN), which can entirely capture the rich information around each voxel, is applied to classify the voxels into airway and non-airway. In addition, a shape reconstruction method is used to refine the airway tree. Results: We evaluate our method on a private dataset and a public dataset from EXACT09. Compared with the segmentation results from other methods, our method demonstrated promising accuracy in complete airway tree segmentation. In the private dataset, the Dice similarity coefficient (DSC), Intersection over Union (IoU), false positive rate (FPR), and sensitivity are 93.5%, 87.8%, 0.015%, and 90.8%, respectively. In the public dataset, the DSC, IoU, FPR, and sensitivity are 95.8%, 91.9%, 0.053% and 96.6%, respectively. Conclusion: The proposed Mif-CNN and CNN-based region growing method segment the airway tree accurately and efficiently in CT scans. Experimental results also demonstrate that the framework is ready for application in computer-aided diagnosis systems for lung disease and other related works. (C) 2022 Elsevier B.V. All rights reserved.
Keyword ：
Airway segmentation Airway segmentation Multi-information fusion convolution neural network Multi-information fusion convolution neural network Voxel classification network Voxel classification network
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Guo, Jinquan , Fu, Rongda , Pan, Lin et al. Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing [J]. | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2022 , 215 . |
| MLA | Guo, Jinquan et al. "Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing" . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 215 (2022) . |
| APA | Guo, Jinquan , Fu, Rongda , Pan, Lin , Zheng, Shaohua , Huang, Liqin , Zheng, Bin et al. Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2022 , 215 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Purpose Accurate recognition of medullary thyroid carcinoma (MTC) is of great importance in medical diagnosis, as MTC is rare but second-most malignant thyroid cancers with a high case-fatality ratio.(1) But there is a lower recognition rate on distinguishing MTC from other thyroid nodules in ultrasound images, even by experienced experts. This paper introduces the computer-aided method to tackle the challenge of recognizing MTC from ultrasound images, including limited MTC samples, and ambiguities among MTC, benign nodules, and papillary thyroid carcinoma (PTC). Methods The recognition of MTC based on large MTC samples of ultrasound images has never been explored, as only one existing work presented a relevant dataset with a limited 21 MTC samples. This study proposes a novel method for primarily differentiating MTC samples from benign nodules and PTC that is the most common thyroid cancer. Our method is a two-stage schema with two important components including a cascaded coarse-to-fine segmentation network and a knowledge-based classification network. The cascaded coarse-to-fine segmentation network incorporates two U-Net++ networks for improving the segmentation results of thyroid nodules. Meanwhile, our knowledge-based classification network extracts and fuses semantic features of solid tissues and calcification for better recognizing the segmented nodules from the ultrasound images. In our experiments, dice similarity coefficient (DSC), intersection over union (IoU), precision, recall, and Hausdorff distance (HD) are adopted for evaluating the segmentation results of thyroid nodules, and accuracy, precision, recall, and F1-score are used for classification evaluation. Results We present a well-annotated dataset including samples of 248 MTC, 240 benign nodules, and 239 PTC. For thyroid nodule segmentation, our designed cascaded segmentation network attains values of 0.776 DSC, 0.689 IoU, 0.778 precision, and 0.821 recall, respectively. By incorporating prior knowledge, our method achieves a mean accuracy of 82.1% in classifying thyroid nodules of MTC, PTC, and benign ones. Especially, our method gains the higher performance in recognizing MTC with an accuracy of 86.8%, compared to nearly 70% diagnosis accuracy of experienced doctors. The experimental results on our Fujian Provincial Hospital dataset further validate the efficiency of our proposed method. Conclusions Our proposed two-stage method incorporates pipelines of thyroid nodules segmentation and classification of MTC, individually. Quantitative and qualitative results indicate that our proposed model achieves accurate segmentation of thyroid nodules. The results also validate that our learning-based framework facilitates the recognition of MTC, which gains better classification accuracy than experienced doctors.
Keyword ：
computer-aided diagnosis computer-aided diagnosis MTC MTC thyroid nodule thyroid nodule ultrasound ultrasound
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Lin , Cai, Yanjing , Lin, Ning et al. A two-stage network with prior knowledge guidance for medullary thyroid carcinoma recognition in ultrasound images [J]. | MEDICAL PHYSICS , 2022 , 49 (4) : 2413-2426 . |
| MLA | Pan, Lin et al. "A two-stage network with prior knowledge guidance for medullary thyroid carcinoma recognition in ultrasound images" . | MEDICAL PHYSICS 49 . 4 (2022) : 2413-2426 . |
| APA | Pan, Lin , Cai, Yanjing , Lin, Ning , Yang, Linxin , Zheng, Shaohua , Huang, Liqin . A two-stage network with prior knowledge guidance for medullary thyroid carcinoma recognition in ultrasound images . | MEDICAL PHYSICS , 2022 , 49 (4) , 2413-2426 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Automatic segmentation and centerline extraction of blood vessels from retinal fundus images is an essential step to measure the state of retinal blood vessels and achieve the goal of auxiliary diagnosis. Combining the information of blood vessel segments and centerline can help improve the continuity of results and performance. However, previous studies have usually treated these two tasks as separate research topics. Therefore, we propose a novel multitask learning network (MSC-Net) for retinal vessel segmentation and centerline extraction. The network uses a multibranch design to combine information between two tasks. Channel and atrous spatial fusion block (CAS-FB) is designed to fuse and correct the features of different branches and different scales. The clDice loss function is also used to constrain the topological continuity of blood vessel segments and centerline. Experimental results on different fundus blood vessel datasets (DRIVE, STARE, and CHASE) show that our method can obtain better segmentation and centerline extraction results at different scales and has better topological continuity than state-of-the-art methods.
Keyword ：
centerline extraction centerline extraction multitask learning multitask learning retinal fundus images retinal fundus images vessel segmentation vessel segmentation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Lin , Zhang, Zhen , Zheng, Shaohua et al. MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction [J]. | APPLIED SCIENCES-BASEL , 2022 , 12 (1) . |
| MLA | Pan, Lin et al. "MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction" . | APPLIED SCIENCES-BASEL 12 . 1 (2022) . |
| APA | Pan, Lin , Zhang, Zhen , Zheng, Shaohua , Huang, Liqin . MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction . | APPLIED SCIENCES-BASEL , 2022 , 12 (1) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
