2 citations · 2 across the 2 of their papers we have counts for
4 papers
Dense Representative Tooth Landmark/axis Detection Network on 3D Model
Guangshun Wei, Zhiming Cui, Jie Zhu +5
Artificial intelligence (AI) technology is increasingly used for digital orthodontics, but one of the challenges is to automatically and accurately detect tooth landmarks and axes.…
Context-aware virtual adversarial training for anatomically-plausible segmentation
Ping Wang, Jizong Peng, Marco Pedersoli +3
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by cl…
Self-paced and self-consistent co-training for semi-supervised image segmentation
Ping Wang, Jizong Peng, Marco Pedersoli +3
Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-su…
Top-Down Shape Abstraction Based on Greedy Pole Selection
Zhiyang Dou, Shiqing Xin, Rui Xu +6
Motivated by the fact that the medial axis transform is able to encode nearly the complete shape, we propose to use as few medial balls as possible to approximate the original encl…