Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Song, Weinan"'
This paper studies a novel energy-based cooperative learning framework for multi-domain image-to-image translation. The framework consists of four components: descriptor, translator, style encoder, and style generator. The descriptor is a multi-head
Externí odkaz:
http://arxiv.org/abs/2306.14448
3D reconstruction of medical imaging from 2D images has become an increasingly interesting topic with the development of deep learning models in recent years. Previous studies in 3D reconstruction from limited X-ray images mainly rely on learning fro
Externí odkaz:
http://arxiv.org/abs/2303.12123
Deep learning models tend to underperform in the presence of domain shifts. Domain transfer has recently emerged as a promising approach wherein images exhibiting a domain shift are transformed into other domains for augmentation or adaptation. Howev
Externí odkaz:
http://arxiv.org/abs/2203.06363
3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object
Externí odkaz:
http://arxiv.org/abs/2108.13004
Convolutional networks (ConvNets) have achieved promising accuracy for various anatomical segmentation tasks. Despite the success, these methods can be sensitive to data appearance variations. Considering the large variability of scans caused by arti
Externí odkaz:
http://arxiv.org/abs/2102.01256
The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines. Moreover, performance gain has been enabled by modelling uncertainty according to empirical evidence. While previous work has widely
Externí odkaz:
http://arxiv.org/abs/2012.12880
Panoramic X-ray (PX) provides a 2D picture of the patient's mouth in a panoramic view to help dentists observe the invisible disease inside the gum. However, it provides limited 2D information compared with cone-beam computed tomography (CBCT), anoth
Externí odkaz:
http://arxiv.org/abs/2003.08413
The encoder-decoder network is widely used to learn deep feature representations from pixel-wise annotations in biomedical image analysis. Under this structure, the performance profoundly relies on the effectiveness of feature extraction achieved by
Externí odkaz:
http://arxiv.org/abs/2002.08406
Label propagation is a popular technique for anatomical segmentation. In this work, we propose a novel deep framework for label propagation based on non-local label fusion. Our framework, named CompareNet, incorporates subnets for both extracting dis
Externí odkaz:
http://arxiv.org/abs/1910.04797
Publikováno v:
In Journal of Colloid And Interface Science September 2022 621:119-130