13 citations · 14 across the 3 of their papers we have counts for
3 papers
cs.CV2024
SP : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation
Shiman Li, Jiayue Zhao, Shaolei Liu +3
Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation da…
eess.IV2024★ 1 cited
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution
Minghong Duan, Linhao Qu, Zhiwei Yang +3
High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinica…
eess.IV2023★ 13 cited
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
Shiman Li, Haoran Wang, Yucong Meng +2
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-gui…