59 citations · 68 across the 6 of their papers we have counts for
4 papers · 1 filter
Which images to label for few-shot medical landmark detection?
Quan Quan, Qingsong Yao, Jun Li +1
The success of deep learning methods relies on the availability of well-labeled large-scale datasets. However, for medical images, annotating such abundant training data often requ…
GAN-based disentanglement learning for chest X-ray rib suppression
Luyi Han, Yuanyuan Lyu, Cheng Peng +1
Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis. However, previous approaches on generating rib-suppr…
Universal Undersampled MRI Reconstruction
Xinwen Liu, Jing Wang, Feng Liu +1
Deep neural networks have been extensively studied for undersampled MRI reconstruction. While achieving state-of-the-art performance, they are trained and deployed specifically for…
You Only Learn Once: Universal Anatomical Landmark Detection
Heqin Zhu, Qingsong Yao, Li Xiao +1
Detecting anatomical landmarks in medical images plays an essential role in understanding the anatomy and planning automated processing. In recent years, a variety of deep neural n…