1.9k citations · 2k across the 6 of their papers we have counts for
6 papers
Textual Inversion and Self-supervised Refinement for Radiology Report Generation
Yuanjiang Luo, Hongxiang Li, Xuan Wu +6
Existing mainstream approaches follow the encoder-decoder paradigm for generating radiology reports. They focus on improving the network structure of encoders and decoders, which l…
Visual Attention Network for Low Dose CT
Wenchao Du, Hu Chen, Peixi Liao +3
Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance. Perfect noise removal and image restoration is intr…
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT
Hu Chen, Yi Zhang, Yunjin Chen +7
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-vie…
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
Hu Chen, Yi Zhang, Mannudeep K. Kalra +5
Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. The current main stream low-dose CT methods…
Low-dose CT denoising with convolutional neural network
Hu Chen, Yi Zhang, Weihua Zhang +4
To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image qu…
Low-Dose CT via Deep Neural Network
Hu Chen, Yi Zhang, Weihua Zhang +4
In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention. However, simply lowering the radiation dose will significantly degrade the imagi…