Lung Cancer Pathological Image Analysis Using a Hidden Potts Model
Nowadays, many biological data are acquired via images. In this article, we study the pathological images scanned from 205 patients with lung cancer with the goal to find out the relationship between the survival time and the spatial distribution of different types of cells, including lymphocyte, st...
Main Authors: | Qianyun Li, Faliu Yi, Tao Wang, Guanghua Xiao, Faming Liang |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2017-06-01
|
Series: | Cancer Informatics |
Online Access: | https://doi.org/10.1177/1176935117711910 |
Similar Items
-
Remote homology search with hidden Potts models.
by: Grey W Wilburn, et al.
Published: (2020-11-01) -
Secure Image-Authentication Schemes With Hidden Double Random-Phase Encoding
by: Faliu Yi, et al.
Published: (2018-01-01) -
Charles Potts Charles Potts
by: Tanea Mara R. Quintanilha
Published: (2008-04-01) -
Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks
by: Faliu Yi, et al.
Published: (2018-02-01) -
Artificial Intelligence in Lung Cancer Pathology Image Analysis
by: Shidan Wang, et al.
Published: (2019-10-01)