Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework

Keyphrases

• Ultrasound Image 100%
• Breast Mass 100%
• Attention-based 100%
• Joint Localization 100%
• Joint Classification 100%
• Model Performance 66%
• Information Sharing 66%
• Data Annotation 66%
• Learning Strategies 66%
• Multi-task Learning 66%
• Semi-supervised Learning 66%
• In Cancer 33%
• Diagnosis Treatment 33%
• Image Data 33%
• Cancer Diagnosis 33%
• Optimal Performance 33%
• Breast Ultrasound 33%
• Attention Mechanism 33%
• Network Architecture 33%
• Personalized Treatment 33%
• Training Model 33%
• Breast Lesions 33%
• Classification Model 33%
• Lesion Localization 33%
• Model Design 33%
• Comprehensive Investigation 33%
• Learning Methods 33%
• Novel Joint 33%
• Location Information 33%
• Suboptimal Performance 33%
• Attention Module 33%
• Individual Tasks 33%
• Model Driven Architecture 33%
• Learning Methodology 33%
• Multiple Attention 33%
• Modular Model 33%
• Asymmetric Model 33%
• Localization Model 33%
• Fully Supervised Learning 33%
• Trunk Network 33%

Computer Science

• Ultrasound Image 100%
• Annotation 66%
• Performance Model 66%
• Multitask Learning 66%
• Semisupervised Learning 66%
• Supervised Learning Strategy 66%
• Experimental Result 33%
• Network Architecture 33%
• Training Model 33%
• Supervised Learning 33%
• Optimal Performance 33%
• Classification Models 33%
• Attention (Machine Learning) 33%
• Location Information 33%
• Model Architecture 33%
• Individual Task 33%
• Individual Component 33%