280 citations · 382 across the 5 of their papers we have counts for
12 papers · 1 filter
Glance-and-Gaze Vision Transformer
Qihang Yu, Yingda Xia, Yutong Bai +3
Recently, there emerges a series of vision Transformers, which show superior performance with a more compact model size than conventional convolutional neural networks, thanks to t…
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
Yingwei Li, Zhuotun Zhu, Yuyin Zhou +4
Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to th…
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Yingda Xia, Dong Yang, Zhiding Yu +7
Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely ex…
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
Yingda Xia, Yi Zhang, Fengze Liu +2
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of…
End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Jinzheng Cai, Yingda Xia, Dong Yang +3
Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentat…
Thickened 2D Networks for Efficient 3D Medical Image Segmentation
Qihang Yu, Yingda Xia, Lingxi Xie +2
There has been a debate in 3D medical image segmentation on whether to use 2D or 3D networks, where both pipelines have advantages and disadvantages. 2D methods enjoy a low inferen…