4k citations · 4k across the 5 of their papers we have counts for
7 papers
Few-Shot Domain Adaptation with Polymorphic Transformers
Shaohua Li, Xiuchao Sui, Jie Fu +7
Deep neural networks (DNNs) trained on one set of medical images often experience severe performance drop on unseen test images, due to various domain discrepancy between the train…
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Shaohua Li, Xiuchao Sui, Xiangde Luo +3
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn ima…
MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning
Xiangde Luo, Guotai Wang, Tao Song +6
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Ne…
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Jieneng Chen, Yongyi Lu, Qihang Yu +6
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segment…
Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency
Xiangde Luo, Wenjun Liao, Jieneng Chen +6
Gross Target Volume (GTV) segmentation plays an irreplaceable role in radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that Convolutional Neural Networks (CNN) hav…
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…