DE-AE: A Dual-Encoder-Based Auto-encoder Framework with Improved Transformer for Anomaly Detection in Medical Imaging

Computer Science

• Anomaly Detection 100%
• Computational Complexity 33%
• Computational Cost 33%
• Convolutional Neural Network 66%
• Detection Method 33%
• Experimental Result 33%
• Image Noise 33%
• Image Reconstruction 66%
• local feature 33%
• Medical Imaging 100%
• Performance Model 33%

Engineering

• Anomaly Detection 100%
• Computational Complexity 33%
• Computational Cost 33%
• Convolutional Neural Network 66%
• Demonstrates 33%
• Experimental Result 33%
• Image Noise 33%
• Image Reconstruction 66%
• Level Feature 33%
• Medical Image 33%

Chemical Engineering

• Auxiliaries 50%
• Neural Network 100%