280 citations · 292 across the 2 of their papers we have counts for
8 papers
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…
Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble
Yingda Xia, Qihang Yu, Wei Shen +3
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population. Screening for PDACs in dynamic contrast-enhanced CT is beneficial for early diagnosi…
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…
An Alarm System For Segmentation Algorithm Based On Shape Model
Fengze Liu, Yingda Xia, Dong Yang +2
It is usually hard for a learning system to predict correctly on rare events that never occur in the training data, and there is no exception for segmentation algorithms. Meanwhile…