30 citations · 57 across the 11 of their papers we have counts for
11 papers
Virtual Full-stack Scanning of Brain MRI via Imputing Any Quantised Code
Yicheng Wu, Tao Song, Zhonghua Wu +5
Magnetic resonance imaging (MRI) is a powerful and versatile imaging technique, offering a wide spectrum of information about the anatomy by employing different acquisition modalit…
Diversified and Personalized Multi-rater Medical Image Segmentation
Yicheng Wu, Xiangde Luo, Zhe Xu +5
Annotation ambiguity due to inherent data uncertainties such as blurred boundaries in medical scans and different observer expertise and preferences has become a major obstacle for…
CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation
Yicheng Wu, Zhonghua Wu, Hengcan Shi +3
New lesion segmentation is essential to estimate the disease progression and therapeutic effects during multiple sclerosis (MS) clinical treatments. However, the expensive data acq…
Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation
Zhonghua Wu, Yicheng Wu, Guosheng Lin +1
Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper…
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
Zhonghua Wu, Yicheng Wu, Guosheng Lin +2
Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy bu…
Image Captioning In the Transformer Age
Yang Xu, Li Li, Haiyang Xu +3
Image Captioning (IC) has achieved astonishing developments by incorporating various techniques into the CNN-RNN encoder-decoder architecture. However, since CNN and RNN do not sha…
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
Yuedong Chen, Qianyi Wu, Chuanxia Zheng +2
Image translation and manipulation have gain increasing attention along with the rapid development of deep generative models. Although existing approaches have brought impressive r…
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
Yicheng Wu, Zhonghua Wu, Qianyi Wu +2
Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often scarce and there are many blurred pixels near the adhesive e…
Sharpness-aware Quantization for Deep Neural Networks
Jing Liu, Jianfei Cai, Bohan Zhuang
Network quantization is a dominant paradigm of model compression. However, the abrupt changes in quantized weights during training often lead to severe loss fluctuations and result…
Mesa: A Memory-saving Training Framework for Transformers
Zizheng Pan, Peng Chen, Haoyu He +3
There has been an explosion of interest in designing high-performance Transformers. While Transformers have delivered significant performance improvements, training such networks i…
Mutual Consistency Learning for Semi-supervised Medical Image Segmentation
Yicheng Wu, Zongyuan Ge, Donghao Zhang +4
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model i…