3 citations · 13 across the 22 of their papers we have counts for
9 papers · 1 filter
Few-shot Class-incremental Learning for Cross-domain Disease Classification
Hao Yang, Weijian Huang, Jiarun Liu +2
The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application. Although existin…
MGA: Medical generalist agent through text-guided knowledge transformation
Weijian Huang, Hao Yang, Cheng Li +3
Multi-modal representation methods have achieved advanced performance in medical applications by extracting more robust features from multi-domain data. However, existing methods u…
Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation
Yousuf Babiker M. Osman, Cheng Li, Weijian Huang +4
Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human…
Universal Generative Modeling for Calibration-free Parallel Mr Imaging
Wanqing Zhu, Bing Guan, Shanshan Wang +2
The integration of compressed sensing and parallel imaging (CS-PI) provides a robust mechanism for accelerating MRI acquisitions. However, most such strategies require the explicit…
Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework
Chen Hu, Cheng Li, Haifeng Wang +3
Image reconstruction from undersampled k-space data plays an important role in accelerating the acquisition of MR data, and a lot of deep learning-based methods have been exploited…
Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning
Kehan Qi, Yu Gong, Xinfeng Liu +3
Noises, artifacts, and loss of information caused by the magnetic resonance (MR) reconstruction may compromise the final performance of the downstream applications. In this paper,…