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Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images
Chia Feng Juang, Ya Wen Chuang, Guan Wen Lin,
I. F. Chung
*
, Ying Chih Lo
*
*
此作品的通信作者
生物醫學資訊研究所
研究成果
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同行評審
3
引文 斯高帕斯（Scopus）
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深入研究「Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images」主題。共同形成了獨特的指紋。
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Keyphrases
Augmentation Approach
20%
Automatically Identify
20%
Classification Results
20%
Cycle-consistent Adversarial Network
20%
Data Augmentation
60%
Deep Convolutional Neural Network (deep CNN)
40%
Deep Learning Methods
100%
Detection Model
20%
Detection Performance
20%
Diagnosis Prediction
20%
F1 Score
20%
Faster R-CNN
20%
Feature Pyramid Network
40%
Generative Data
40%
Glomeruli Segmentation
100%
Glomerulus
100%
Glomerulus Detection
100%
HE Staining
20%
Histopathological Images
100%
Interpretation Process
20%
Network Approach
20%
Neural Network
20%
Outcome Prediction
20%
PAS Stain
20%
Patch-based
20%
Pathologist
20%
Prediction Information
20%
Renal Pathology
100%
Scalable Methods
20%
Standardized Method
20%
Training Data
20%
Xception
80%
Computer Science
classification result
25%
Classification Stage
25%
Convolutional Neural Network
25%
Data Augmentation
75%
Deep Convolutional Neural Networks
50%
Deep Learning Method
100%
Detection Performance
25%
Discriminability
25%
Generative Adversarial Networks
25%
Generative Data
50%
Neural Network Model
25%
Process Interpretation
25%
Scalable Method
25%
Training Data
25%
Xception
100%
Engineering
Convolutional Neural Network
100%
Deep Learning Method
100%
Detection Performance
33%
Network Model
33%
Medicine and Dentistry
Glomerulus
100%
Pathologist
12%
Prognosis
12%
Renal Pathology
100%
Sensitivity and Specificity
25%
Spine
12%
Chemical Engineering
Deep Learning Method
100%
Neural Network
100%