79 citations · 92 across the 5 of their papers we have counts for
7 papers
Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation
Cheng Xue, Qiao Deng, Xiaomeng Li +2
The superior performance of CNN on medical image analysis heavily depends on the annotation quality, such as the number of labeled image, the source of image, and the expert experi…
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Lequan Yu, Zhicheng Zhang, Xiaomeng Li +1
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence…
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
Huazhu Fu, Fei Li, Xu Sun +22
Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevat…
CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
Xiaomeng Li, Xiaowei Hu, Lequan Yu +3
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophtha…
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
Lequan Yu, Shujun Wang, Xiaomeng Li +2
Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and time-consuming to annotate data for medical image segmenta…
Difficulty-aware Meta-learning for Rare Disease Diagnosis
Xiaomeng Li, Lequan Yu, Yueming Jin +3
Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a…