Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Liu, Fengze"'
Recent developments in Transformers have achieved notable strides in enhancing video comprehension. Nonetheless, the O($N^2$) computation complexity associated with attention mechanisms presents substantial computational hurdles when dealing with the
Externí odkaz:
http://arxiv.org/abs/2402.18577
Autor:
Tian, Lin, Li, Zi, Liu, Fengze, Bai, Xiaoyu, Ge, Jia, Lu, Le, Niethammer, Marc, Ye, Xianghua, Yan, Ke, Jin, Daikai
Image registration is a fundamental medical image analysis task. Ideally, registration should focus on aligning semantically corresponding voxels, i.e., the same anatomical locations. However, existing methods often optimize similarity measures compu
Externí odkaz:
http://arxiv.org/abs/2311.14986
Shape information is a strong and valuable prior in segmenting organs in medical images. However, most current deep learning based segmentation algorithms have not taken shape information into consideration, which can lead to bias towards texture. We
Externí odkaz:
http://arxiv.org/abs/2207.02529
Autor:
Zhou, Yuyin, Li, Xianhang, Liu, Fengze, Wei, Qingyue, Chen, Xuxi, Yu, Lequan, Xie, Cihang, Lungren, Matthew P., Xing, Lei
Deep neural networks have shown great success in representation learning. However, when learning with noisy labels (LNL), they can easily overfit and fail to generalize to new data. This paper introduces a simple and effective method, named Learning
Externí odkaz:
http://arxiv.org/abs/2202.04291
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. The development of automatic segmentation systems from multi-phase CT for splenic vascular injury can augment severity grading for improving clinical decision supp
Externí odkaz:
http://arxiv.org/abs/2201.00942
Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always designs end-to-end architectures and aims at learning the difference betwee
Externí odkaz:
http://arxiv.org/abs/2111.11745
Autor:
Liu, Fengze, Yan, Ke, Harrison, Adam, Guo, Dazhou, Lu, Le, Yuille, Alan, Huang, Lingyun, Xie, Guotong, Xiao, Jing, Ye, Xianghua, Jin, Dakai
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm SAM, which is capable of computing dense anatomical/semantic correspondences between two images at t
Externí odkaz:
http://arxiv.org/abs/2109.11572
Autor:
Xia, Yingda, Yang, Dong, Yu, Zhiding, Liu, Fengze, Cai, Jinzheng, Yu, Lequan, Zhu, Zhuotun, Xu, Daguang, Yuille, Alan, Roth, Holger
Publikováno v:
Medical Image Analysis, 2020
Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely expensive in the field of medical image analysis. Unlabeled data, on the
Externí odkaz:
http://arxiv.org/abs/2006.16806
Autor:
Liu, Fengze, Cai, Jinzheng, Huo, Yuankai, Cheng, Chi-Tung, Raju, Ashwin, Jin, Dakai, Xiao, Jing, Yuille, Alan, Lu, Le, Liao, ChienHung, Harrison, Adam P
Multi-modal image registration is a challenging problem that is also an important clinical task for many real applications and scenarios. As a first step in analysis, deformable registration among different image modalities is often required in order
Externí odkaz:
http://arxiv.org/abs/2005.12209
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image a
Externí odkaz:
http://arxiv.org/abs/2003.08440