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Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification

Autor: Zhang, Zhenxi, Wang, Yanyang, Wu, Yao, Wu, Weifei

Developmental Canal Stenosis (DCS) quantification is crucial in cervical spondylosis screening. Compared with quantifying DCS manually, a more efficient and time-saving manner is provided by deep keypoint localization networks, which can be implement

Externí odkaz: http://arxiv.org/abs/2309.06825

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Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction

Autor: Pan, Jiayi, Zhang, Heye, Wu, Weifei, Gao, Zhifan, Wu, Weiwen

Decreasing projection views to lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a Multi-domain Integrative Swin Transformer network (MIST-net) was developed in this article. First, M

Externí odkaz: http://arxiv.org/abs/2111.14831

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Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery

Autor: Xu, Moran, Hu, Dianlin, Wu, Weifei, Wu, Weiwen

Image restoration is a typical ill-posed problem, and it contains various tasks. In the medical imaging field, an ill-posed image interrupts diagnosis and even following image processing. Both traditional iterative and up-to-date deep networks have a

Externí odkaz: http://arxiv.org/abs/2102.06102

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Improved Material Decomposition with a Two-step Regularization for spectral CT

Autor: Wu, Weiwen, Chen, Peijun, Vardhanabhuti, Vince, Wu, Weifei, Yu, Hengyong

One of the advantages of spectral computed tomography (CT) is it can achieve accurate material components using the material decomposition methods. The image-based material decomposition is a common method to obtain specific material components, and

Externí odkaz: http://arxiv.org/abs/1910.05259

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