Výsledky vyhledávání - "Li, Zhongsen"

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Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI Reconstruction

Autor: Li, Zhongsen, Chen, Wenxuan, Wang, Shuai, Liu, Chuyu, Zou, Qing, Li, Rui

The inductive bias of the convolutional neural network (CNN) can be a strong prior for image restoration, which is known as the Deep Image Prior (DIP). Recently, DIP is utilized in unsupervised dynamic MRI reconstruction, which adopts a generative mo

Externí odkaz: http://arxiv.org/abs/2403.15770

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Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution

Autor: Chen, Wenxuan, Wu, Sirui, Wang, Shuai, Li, Zhongsen, Yang, Jia, Yao, Huifeng, Song, Xiaolei

Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissue from different perspectives and has many clinical applications. By utilizing the complementary information among different modalities, multi-contrast super-resolu

Externí odkaz: http://arxiv.org/abs/2307.02148

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Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation

Autor: Wang, Shuai, Yan, Zipei, Zhang, Daoan, Li, Zhongsen, Wu, Sirui, Chen, Wenxuan, Li, Rui

Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, esp

Externí odkaz: http://arxiv.org/abs/2305.07883

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Prototype Knowledge Distillation for Medical Segmentation with Missing Modality

Autor: Wang, Shuai, Yan, Zipei, Zhang, Daoan, Wei, Haining, Li, Zhongsen, Li, Rui

Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for medical image segmentation. However, collecting multi-modal data in clinical is difficult due to the limitation of the scan time and other

Externí odkaz: http://arxiv.org/abs/2303.09830

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Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

Autor: Li, Zhongsen, Sun, Aiqi, Liu, Chuyu, Wei, Haining, Wang, Shuai, Fu, Mingzhu, Li, Rui

Objective. Imaging dynamic object with high temporal resolution is challenging in magnetic resonance imaging (MRI). Partial separable (PS) model was proposed to improve the imaging quality by reducing the degrees of freedom of the inverse problem. Ho

Externí odkaz: http://arxiv.org/abs/2210.00493

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