4 citations · 9 across the 16 of their papers we have counts for
7 papers · 1 filter
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
Weijian Huang, Cheng Li, Hong-Yu Zhou +6
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucia…
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…
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…
AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Hui Sun, Cheng Li, Boqiang Liu +3
Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to t…