Lung cancer lesion detection in histopathology images using graph-based sparse PCA network

Keyphrases

• Lung Cancer 100%
• Graph-based 100%
• Lesion Detection 100%
• Histopathological Images 100%
• Precancerous Lesions 100%
• Sparse PCA 100%
• PCA Network 100%
• Genetically Engineered Mouse Models 40%
• Hematoxylin 40%
• Sparse Principal Component Analysis 40%
• Eosin 40%
• Machine Learning Approach 20%
• Histogram 20%
• Detection Accuracy 20%
• Automated Detection 20%
• Four-step 20%
• Patient Survival 20%
• Lung 20%
• Therapeutic Target 20%
• Tumor Burden 20%
• Hashing 20%
• Mouse Tumor Model 20%
• Accuracy Analysis 20%
• Existing Algorithms 20%
• Proposed Architecture 20%
• Complex Disease 20%
• Precision-recall 20%
• Feature Extraction 20%
• For Indexing 20%
• Efficient Analysis 20%
• Receiver Operating Characteristic 20%
• Manual Inspection 20%
• Improved Detection 20%
• Multiple Stages 20%
• Area under the Curve 20%
• Support Vector Machine Classification 20%
• Recall Rate 20%
• Simple Machine 20%
• Filter Bank 20%
• Subjective Bias 20%
• Principal Component Analysis Network (PCANet) 20%
• Convolutional Networks 20%
• Computer-aided Diagnostic Tool 20%
• Binary Hashing 20%
• Block Histogram 20%
• Tanimoto Coefficient 20%
• Purkinje Cell Antibody 2 (PCA-2) 20%

Computer Science

• Component Analysis 100%
• Hashing 100%
• Support Vector Machine 100%
• Principal Components 100%
• Early Detection 50%
• Machine Learning Approach 50%
• Detection Accuracy 50%
• Extracted Feature 50%
• Convolutional Network 50%
• Filter Bank 50%
• Complex Disease 50%
• F-Score 50%