185 citations · 352 across the 19 of their papers we have counts for
8 papers · 1 filter
Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision
Xiangde Luo, Minhao Hu, Wenjun Liao +4
Medical image segmentation plays an irreplaceable role in computer-assisted diagnosis, treatment planning, and following-up. Collecting and annotating a large-scale dataset is cruc…
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
Kexin Ding, Mu Zhou, Zichen Wang +4
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales. The development…
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation
Ran Gu, Jingyang Zhang, Rui Huang +3
Domain generalizable model is attracting increasing attention in medical image analysis since data is commonly acquired from different institutes with various imaging protocols and…
Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss
Wenhui Lei, Haochen Mei, Zhengwentai Sun +7
Nasopharyngeal Carcinoma (NPC) is a leading form of Head-and-Neck (HAN) cancer in the Arctic, China, Southeast Asia, and the Middle East/North Africa. Accurate segmentation of Orga…
Annotation-Efficient Learning for Medical Image Segmentation based on Noisy Pseudo Labels and Adversarial Learning
Lu Wang, Dong Guo, Guotai Wang +1
Despite that deep learning has achieved state-of-the-art performance for medical image segmentation, its success relies on a large set of manually annotated images for training tha…
Learning Euler's Elastica Model for Medical Image Segmentation
Xu Chen, Xiangde Luo, Yitian Zhao +3
Image segmentation is a fundamental topic in image processing and has been studied for many decades. Deep learning-based supervised segmentation models have achieved state-of-the-a…