Co‐trained convolutional neural networks for automated detection of prostate cancer in multi‐parametric MRI
By Xin Yang, Jun Yang, Hung Le Minh, Liang Wang, Kwang-Ting Cheng. 2017
143 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Xiaosong Wang
- Arvid Lundervold
- Maryellen Lissak Giger
- Berkman Sahiner
- Ronald M. Summers
- Lubomir M. Hadjiiski
- Karen Drukker
- Kenny H. Cha
In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
- Lubomir M. Hadjiiski
- Xiaosong Wang
- Ronald M. Summers
- Arvid Lundervold
- Kenny H. Cha
- Berkman Sahiner
- Karen Drukker
- Aria Pezeshk
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “benign hyperplasia in a collection of 172 patients with more than 2500 morphologic 2D T2-w MR images [291] Designed a system which can concurrently identify the presence of PCa in an image and localize lesions based”
- Deep learning in medical imaging and radiation therapy. (2018, background): “322 Yang et al. 240 created a synthetic CT dataset through the use of the Radon transform for a known object and modeled different exposure conditions through adding noise to the data, for training a CNN to estimate high-dose projections from low-dose ones.”