52 citations · 54 across the 4 of their papers we have counts for
4 papers
STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training
Ziyan Huang, Haoyu Wang, Zhongying Deng +8
Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentatio…
Token Sparsification for Faster Medical Image Segmentation
Lei Zhou, Huidong Liu, Joseph Bae +3
Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is s…
Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation
Zhongying Deng, Da Li, Junjun He +2
Target domain pseudo-labelling has shown effectiveness in unsupervised domain adaptation (UDA). However, pseudo-labels of unlabeled target domain data are inevitably noisy due to t…
Learning with Explicit Shape Priors for Medical Image Segmentation
Xin You, Junjun He, Jie Yang +1
Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the field of medical image…