A performance comparison of low- and high-level features learned by deep convolutional neural networks in epithelium and stroma classification.
Yue DuRoy ZhangAbolfazl ZargariTheresa C. ThaiCamille C. GundersonKatherine M. MoxleyHong LiuBin ZhengYuchen QiuPublished in: Medical Imaging: Digital Pathology (2018)
Keyphrases
- low and high level
- feature vectors
- low level
- feature extraction
- classification accuracy
- classification models
- classification method
- convolutional neural networks
- feature set
- feature space
- benchmark datasets
- pattern recognition
- classification process
- extracted features
- svm classifier
- pattern classification
- classification scheme
- extracting features
- decision trees
- text classification
- co occurrence
- gender classification
- classification algorithm
- high dimensionality
- feature reduction
- feature weights
- class specific
- learning phase
- feature analysis
- discriminative features
- feature extraction and classification
- textural features
- features extraction
- image features
- class labels
- feature representation
- eeg signals
- dimensionality reduction
- image classification
- roc curve
- machine learning
- writer identification
- unsupervised learning
- positive training examples
- computer vision