Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach
Jintao Ru,
Zili Zhu,
Jialin Shi
Abstract:Background
Breast cancer is the most common cancer among women, and ultrasound is a usual tool for early screening. Nowadays, deep learning technique is applied as an auxiliary tool to provide the predictive results for doctors to decide whether to make further examinations or treatments. This study aimed to develop a hybrid learning approach for breast ultrasound classification by extracting more potential features from local and multi-center ultrasound data.
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