96 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of Medical Sciences & Peking Union Medical CollegeCN3 papers
- The University of Texas Southwestern Medical CenterUS2 papers
- Cedars-Sinai Medical CenterUS1 paper
- Chinese Academy of SciencesCN1 paper
- Chinese University of Hong KongHK1 paper
- Group Sense (China)CN1 paper
- Peking Union Medical College HospitalCN1 paper
- Rutgers, The State University of New JerseyUS1 paper
- Southern Medical UniversityCN1 paper
- Southwestern Medical CenterUS1 paper
- The University of Texas MD Anderson Cancer CenterUS1 paper
- Xidian UniversityCN1 paper
5 papers
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
Yunhe Gao, Rui Huang, Yiwei Yang +7
Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid…
Deep Learning-based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer
Jie Fu, Xinran Zhong, Ning Li +6
Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly designed…
ConvPath: A Software Tool for Lung Adenocarcinoma Digital Pathological Image Analysis Aided by Convolutional Neural Network
Shidan Wang, Tao Wang, Lin Yang +11
The spatial distributions of different types of cells could reveal a cancer cell growth pattern, its relationships with the tumor microenvironment and the immune response of the bo…
Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction
Botong Wu, Zhen Zhou, Jianwei Wang +1
Refer to the literature of lung nodule classification, many studies adopt Convolutional Neural Networks (CNN) to directly predict the malignancy of lung nodules with original thora…
Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer
Hongxia Hao, Zhiguo Zhou, Shulong Li +10
Purpose To develop and demonstrate a novel tumor shell feature for predicting distant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC) patients. Patients and…