1 code implementation • 17 Jan 2024 • Yiqun Lin, Liang Pan, Yi Li, Ziwei Liu, Xiaomeng Li
In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions.
1 code implementation • 8 Dec 2023 • Tianqi Xiang, Wenjun Yue, Yiqun Lin, Jiewen Yang, Zhenkun Wang, Xiaomeng Li
Performing magnetic resonance imaging (MRI) reconstruction from under-sampled k-space data can accelerate the procedure to acquire MRI scans and reduce patients' discomfort.
2 code implementations • 15 Apr 2023 • Huimin Wu, Xiaomeng Li, Yiqun Lin, Kwang-Ting Cheng
This study investigates barely-supervised medical image segmentation where only few labeled data, i. e., single-digit cases are available.
1 code implementation • 12 Mar 2023 • Yiqun Lin, Zhongjin Luo, Wei Zhao, Xiaomeng Li
In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstruction from extremely sparse (fewer than 10) projection views at an ultrafast speed.
1 code implementation • 30 Sep 2022 • Wanqin Ma, Huifeng Yao, Yiqun Lin, Jiarong Guo, Xiaomeng Li
Our main goal is to improve the quality of pseudo labels under extreme MRI Analysis with various domains.
1 code implementation • 7 May 2022 • Yiqun Lin, Huifeng Yao, Zezhong Li, Guoyan Zheng, Xiaomeng Li
Our framework leverages label distribution to encourage the network to put more effort into learning cartilage parts.
1 code implementation • 18 Apr 2022 • Xunguang Wang, Yiqun Lin, Xiaomeng Li
On the one hand, CgAT generates the worst adversarial examples as augmented data by maximizing the Hamming distance between the hash codes of the adversarial examples and the center codes.
1 code implementation • CVPR 2022 • Xiaoxiao Liang, Yiqun Lin, Huazhu Fu, Lei Zhu, Xiaomeng Li
In this paper, we present a Random Sampling Consensus Federated learning, namely RSCFed, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients.
1 code implementation • ICCV 2021 • Bingchen Gong, Yinyu Nie, Yiqun Lin, Xiaoguang Han, Yizhou Yu
Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details.
no code implementations • 9 Jul 2021 • Yiqun Lin, Lichang Chen, Haibin Huang, Chongyang Ma, Xiaoguang Han, Shuguang Cui
Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds.
no code implementations • NeurIPS 2020 • Yinyu Nie, Yiqun Lin, Xiaoguang Han, Shihui Guo, Jian Chang, Shuguang Cui, Jian Jun Zhang
Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points.
1 code implementation • CVPR 2020 • Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han
We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis.