Y
Yueyue Wang
Researcher at Fudan University
Publications - 12
Citations - 146
Yueyue Wang is an academic researcher from Fudan University. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 3, co-authored 4 publications receiving 63 citations.
Papers
More filters
Journal ArticleDOI
Organ at Risk Segmentation in Head and Neck CT Images Using a Two-Stage Segmentation Framework Based on 3D U-Net
TL;DR: In this article, a two-stage segmentation framework based on 3D U-Net is proposed for organs at risk (OARs) segmentation, where the segmentation of each OAR is decomposed into two subtasks.
Journal ArticleDOI
Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters.
Danjun Song,Yueyue Wang,Wen-Tao Wang,Yining Wang,Jia-Bin Cai,Kai Zhu,Minzhi Lv,Qiang Gao,Jian Zhou,Jia Fan,Sheng-Xiang Rao,Manning Wang,Xiaoying Wang +12 more
TL;DR: The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient’s prognosis.
Posted Content
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation.
TL;DR: Wang et al. as mentioned in this paper proposed a pure transformer-based U-shaped Encoder-Decoder architecture with skip-connections for local-global semantic feature learning for medical image segmentation.
Journal ArticleDOI
Selecting proper combination of mpMRI sequences for prostate cancer classification using multi-input convolutional neuronal network
Yueyue Wang,Manning Wang +1 more
TL;DR: Higher performance can be achieved by selecting a suitable subset of the mpMRI sequences in PCa classification, which was much higher than currently published results and ranked first out of more than 1500 entries submitted to the challenge at the time of submission of this paper.
Journal ArticleDOI
An abnormal driving behavior recognition algorithm based on the temporal convolutional network and soft thresholding
TL;DR: This work proposes an algorithm based on Soft Thresholding and Temporal Convolutional Network (S‐TCN) for driving behavior recognition that outperforms best state‐of‐the‐art baselines by 2.24%.