3 accepted papers
3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to annotating new domains. Self-training is a competitive approach for this task but its performance is limited by different sensor sampling patterns (i.e. variations in point density) and incomplete training strategies. In th…
Unsupervised domain adaptation commonly uses cycle generative networks to produce synthesis data from source to target domains. Unfortunately, translated samples cannot effectively preserve semantic information from input sources, resulting in bad or low adaptability of the network to segment target…
Vessel and neurovascular bundle localization plays an essential role in endoscopic and robotic surgery. It still remains challenging to spare vessels and neurovascular bundles to avoid inadvertent injury due to limited visual and tactile perception of surgeons. This work assumes that surgeons have g