output
20192022
most citedAnnotation-efficient deep learning for automatic medical image segmentation

313 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing eess.IVShow all

5 papers · 1 filter

eess.IV2022★ 27 cited

Quad-Net: Quad-domain Network for CT Metal Artifact Reduction

Zilong Li, Qi Gao, Yaping Wu +5

Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various…

eess.IV2020★ 313 cited

Annotation-efficient deep learning for automatic medical image segmentation

Shanshan Wang, Cheng Li, Rongpin Wang +12

Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training…

eess.IV2019★ 4 cited

Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation

Cheng Li, Hui Sun, Zaiyi Liu +3

Multi-modal magnetic resonance imaging (MRI) is essential in clinics for comprehensive diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images tend…

eess.IV2019★ 77 cited

CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke

Hao Yang, Weijian Huang, Kehan Qi +5

Segmenting stroke lesions from T1-weighted MR images is of great value for large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there are great challenges with th…

eess.IV2019★ 139 cited

X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies

Kehan Qi, Hao Yang, Cheng Li +4

The morbidity of brain stroke increased rapidly in the past few years. To help specialists in lesion measurements and treatment planning, automatic segmentation methods are critica…