EpiUNet: Stain-Style Transfer Model for Histology Image Based on Generative Adversarial Network
Abstract
References
Index Terms
- EpiUNet: Stain-Style Transfer Model for Histology Image Based on Generative Adversarial Network
Recommendations
Lung cancer histopathology image classification using transfer learning with convolution neural network model
BACKGROUND:Lung cancer (LC) is a harmful malignant tumor and potentially lethal illness. Therefore, early detection of LC is an urgent need, and dependent on the type of histology and the type of disease. The use of deep learning algorithms (DL) is ...
Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey
AbstractLung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions ...
Graphical abstractDisplay Omitted
Highlights- This survey focuses on lung cancer diagnosis from computed tomography data.
- ...
Attribute-guided image generation of three-dimensional computed tomography images of lung nodules using a generative adversarial network
Abstract PurposeTo develop and evaluate a three-dimensional (3D) generative model of computed tomography (CT) images of lung nodules using a generative adversarial network (GAN). To guide the GAN, lung nodule size was used.
Materials and methodsA ...
Highlights- A generative adversarial network (GAN) was trained for 3D CT lung nodule images.
- To guide the GAN, lung nodule size was used.
- The images generated by GAN were difficult to distinguish from true images.
- Using the generated ...
Comments
Information & Contributors
Information
Published In
![cover image ACM Conferences](/cms/asset/30828691-bcc4-4dd9-a56d-35cf891e0b42/3698587.cover.jpg)
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Funding Sources
- Guangzhou Science, Technology and Innovation Commission(CN)
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 28Total Downloads
- Downloads (Last 12 months)28
- Downloads (Last 6 weeks)10
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in