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1,292 results on '"Zhan, Yiqiang"'

16. Prompt-Based Segmentation Model of Anatomical Structures and Lesions in CT Images

Author

Ouyang, Xi, Gu, Dongdong, Li, Xuejian, Zhou, Wenqi, Chen, Qianqian, Zhan, Yiqiang, Zhou, Xiang, Shi, Feng, Xue, Zhong, Shen, Dinggang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Linguraru, Marius George, editor, Dou, Qi, editor, Feragen, Aasa, editor, Giannarou, Stamatia, editor, Glocker, Ben, editor, Lekadir, Karim, editor, and Schnabel, Julia A., editor

Published
2024
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17. Contrastive Learning-Based Breast Tumor Segmentation in DCE-MRI

Author

Guo, Shanshan, Zhang, Jiadong, Gu, Dongdong, Gao, Fei, Zhan, Yiqiang, Xue, Zhong, Shen, Dinggang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Xiaohuan, editor, Xu, Xuanang, editor, Rekik, Islem, editor, Cui, Zhiming, editor, and Ouyang, Xi, editor

Published
2024
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19. A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion

Author

Meng, Xiangxi, Gu, Yuning, Pan, Yongsheng, Wang, Nizhuan, Xue, Peng, Lu, Mengkang, He, Xuming, Zhan, Yiqiang, and Shen, Dinggang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Multi-modal medical image completion has been extensively applied to alleviate the missing modality issue in a wealth of multi-modal diagnostic tasks. However, for most existing synthesis methods, their inferences of missing modalities can collapse into a deterministic mapping from the available ones, ignoring the uncertainties inherent in the cross-modal relationships. Here, we propose the Unified Multi-Modal Conditional Score-based Generative Model (UMM-CSGM) to take advantage of Score-based Generative Model (SGM) in modeling and stochastically sampling a target probability distribution, and further extend SGM to cross-modal conditional synthesis for various missing-modality configurations in a unified framework. Specifically, UMM-CSGM employs a novel multi-in multi-out Conditional Score Network (mm-CSN) to learn a comprehensive set of cross-modal conditional distributions via conditional diffusion and reverse generation in the complete modality space. In this way, the generation process can be accurately conditioned by all available information, and can fit all possible configurations of missing modalities in a single network. Experiments on BraTS19 dataset show that the UMM-CSGM can more reliably synthesize the heterogeneous enhancement and irregular area in tumor-induced lesions for any missing modalities.

Published
2022

22. Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution

Author

Zhuang, Zixu, Si, Liping, Wang, Sheng, Xuan, Kai, Ouyang, Xi, Zhan, Yiqiang, Xue, Zhong, Zhang, Lichi, Shen, Dinggang, Yao, Weiwu, and Wang, Qian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying that only a small portion of voxels in knee MRI can contribute to the cartilage defect assessment; heterogeneous scanning protocols further challenge the feasibility of the CNNs in clinical practice; the CNN-based knee cartilage evaluation results lack interpretability. To address these challenges, we model the cartilages structure and appearance from knee MRI into a graph representation, which is capable of handling highly diverse clinical data. Then, guided by the cartilage graph representation, we design a non-Euclidean deep learning network with the self-attention mechanism, to extract cartilage features in the local and global, and to derive the final assessment with a visualized result. Our comprehensive experiments show that the proposed method yields superior performance in knee cartilage defect assessment, plus its convenient 3D visualization for interpretability., Comment: 10 pages, 4 figures

Published
2022

36. HC-Net: Hybrid Classification Network for Automatic Periodontal Disease Diagnosis

Author

Mei, Lanzhuju, Fang, Yu, Cui, Zhiming, Deng, Ke, Wang, Nizhuan, He, Xuming, Zhan, Yiqiang, Zhou, Xiang, Tonetti, Maurizio, Shen, Dinggang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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37. HENet: Hierarchical Enhancement Network for Pulmonary Vessel Segmentation in Non-contrast CT Images

Author

Zhou, Wenqi, Zhang, Xiao, Gu, Dongdong, Wang, Sheng, Huo, Jiayu, Zhang, Rui, Jiang, Zhihao, Shi, Feng, Xue, Zhong, Zhan, Yiqiang, Ouyang, Xi, Shen, Dinggang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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46. DS-GCNs: Connectome Classification Using Dynamic Spectral Graph Convolution Networks with Assistant Task Training

Author

Xing, Xiaodan, Li, Qingfeng, Wei, Hao, Zhang, Minqing, Zhan, Yiqiang, Zhou, Xiang Sean, Xue, Zhong, and Shi, Feng

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition
Abstract

Functional Connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. However, a FC matrix is neither a natural image which contains shape and texture information, nor a vector of independent features, which renders the extracting of efficient features from matrices as a challenging problem. A brain network, also named as connectome, could forma a graph structure naturally, the nodes of which are brain regions and the edges are interregional connectivity. Thus, in this study, we proposed novel graph convolutional networks (GCNs) to extract efficient disease-related features from FC matrices. Considering the time-dependent nature of brain activity, we computed dynamic FC matrices with sliding-windows and implemented a graph convolution based LSTM (long short term memory) layer to process dynamic graphs. Moreover, the demographics of patients were also used to guide the classification. However, unlike in conventional methods where personal information, i.e., gender and age were added as extra inputs, we argue that this kind of approach may not actually improve the classification performance, for such personal information given in dataset was usually balanced distributed. In this paper, we proposed to utilize the demographic information as extra outputs and to share parameters among three networks predicting subject status, gender and age, which serve as assistant tasks. We tested the performance of the proposed architecture in ADNI II dataset to classify Alzheimer's disease patients from normal controls. The classification accuracy, sensitivity and specificity reach 0.90, 0.92 and 0.89 on ADNI II dataset., Comment: Number of pages: 22 Word count in abstract: 252, word count in manuscript: 3910 This manuscript includes 3 tables and 9 figures

Published
2019

50. Covalent bonding strategy to enable non-volatile organic cation perovskite for highly stable and efficient solar cells

Author

Liu, Kai, Rafique, Saqib, Musolino, Stefania F., Cai, Zenghua, Liu, Fengcai, Li, Xiaoguo, Yuan, Yongbo, Bao, Qinye, Yang, Yingguo, Chu, Jiao, Peng, Xinxin, Nie, Cengao, Yuan, Wei, Zhang, Sidi, Wang, Jiao, Pan, Yiyi, Zhang, Haijuan, Cai, Xia, Shi, Zejiao, Li, Chongyuan, Wang, Haoliang, Deng, Liangliang, Hu, Tianxiang, Wang, Yaxin, Wang, Yanyan, Chen, Shiyou, Shi, Lei, Ayala, Paola, Wulff, Jeremy E., Yu, Anran, and Zhan, Yiqiang

Published
2023
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