activity
20182023
most citedAnnotation-efficient deep learning for automatic medical image segmentation

313 citations · 719 across the 30 of their papers we have counts for

collaborators
Showing 2023Show all

8 papers · 1 filter

eess.IV2023★ 1 cited

Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation

Yousuf Babiker M. Osman, Cheng Li, Weijian Huang +1

Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotati…

cs.CV2023★ 57 cited

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…

eess.IV2023

Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search

Ruoyou Wu, Cheng Li, Juan Zou +1

Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstructio…

eess.IV2023

FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction

Ruoyou Wu, Cheng Li, Juan Zou +1

Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions. Federated learning,…

eess.IV2023

Self-Supervised Federated Learning for Fast MR Imaging

Juan Zou, Cheng Li, Ruoyou Wu +3

Federated learning (FL) based magnetic resonance (MR) image reconstruction can facilitate learning valuable priors from multi-site institutions without violating patient's privacy…

eess.IV2023

Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data

Ruoyou Wu, Cheng Li, Juan Zou +3

Deep learning-based methods have achieved encouraging performances in the field of magnetic resonance (MR) image reconstruction. Nevertheless, to properly learn a powerful and robu…

cs.CV2023★ 3 cited

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…

cs.CV2023★ 1 cited

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…