Exploiting Hierarchical Interactions for Protein Surface Learning

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.

DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models

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.

Image Denoising MRI Reconstruction

Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation

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.

Image Segmentation Pancreas Segmentation +3

Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

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.

Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels

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.

Domain Generalization Image Segmentation +3

Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation

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.

CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval

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.

Adversarial Attack Adversarial Defense +4

RSCFed: Random Sampling Consensus Federated Semi-supervised Learning

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.

Federated Learning

ME-PCN: Point Completion Conditioned on Mask Emptiness

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.

Task-Aware Sampling Layer for Point-Wise Analysis

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.

Keypoint Detection Point Cloud Completion +1

Skeleton-bridged Point Completion: From Global Inference to Local Adjustment

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.

Surface Reconstruction

FPConv: Learning Local Flattening for Point Convolution

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.

3D Object Classification Scene Segmentation