Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.
By Koichiro Yasaka, Hiroyuki Akai, Osamu Abe, Shigeru Kiryu. Radiology, 2017
595 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/4d3f4c400d964d714f75ea7310de39779308ae89
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Dinggang Shen
- Erica Schmidt
- Qian Wang
- Ronald M. Summers
- Maithra Raghu
- Arvid Lundervold
- Meghavi Rana
- Megha Bhushan
In Radiology, Nuclear Medicine and Imaging
- Lubomir M. Hadjiiski
- Xiaosong Wang
- Ronald M. Summers
- Kenny H. Cha
- Dinggang Shen
- Berkman Sahiner
- Karen Drukker
- Aria Pezeshk
Citations in the corpus
- A Survey of Deep Learning for Scientific Discovery (2020, background): “Examples range from ophthalmology [72], radiology (2D x-rays and 3D CT scans) [258, 5, 185], Instance Segmentation tasks.”
- Convolutional neural networks: an overview and application in radiology (2018, methodology): “One previous study used CT image sets of liver masses over three phases (non-enhanced CT, and enhanced CT in arterial and delayed phases) for the classification of liver masses with 2D-CNN [8].”
- Deep learning in medical imaging and radiation therapy. (2018, background): “179 Assessing liver tissue lends itself to DCNNs in the task of staging liver fibrosis on MRI by Yasaka et al. 216 and ultrasonic fatty liver disease characterization by Bharath et al. 217”
- Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia (2020, methodology): “use CNN for differentiation of liver masses on dynamic contrast agentenhanced CT images [20].”