76 citations · 211 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
Linhao Qu, Minghong Duan, Zhiwei Yang +2
Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. Though implicit neural network-based methods have…
Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation
Xiaoyu Liu, Linhao Qu, Ziyue Xie +3
Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiat…
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…
Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling
Chunli Qin, Demin Yao, Han Zhuang +3
Accurate identification and localization of the vertebrae in CT scans is a critical and standard preprocessing step for clinical spinal diagnosis and treatment. Existing methods ar…