Breast cancer molecular subtype prediction method based on model driver element learning

• Main Authors: WANG YIHANG, LIU YUAN, PAN XIANG, ZHANG JIRU, XIE ZHENPING, ZHANG YAN, LI LIHUA, HU SHUDONG, LYU TIANXU
• Format: Patent
• Language: Chinese; English
• Published: 15.03.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a breast cancer molecular subtype prediction method based on model driving element learning, and the method comprises the steps: obtaining a dynamic enhanced magnetic resonance image and a label of the dynamic enhanced magnetic resonance image through a breast cancer database; processing the dynamic enhanced magnetic resonance image to obtain dynamic enhanced magnetic resonance volume data, and matching the dynamic enhanced magnetic resonance volume data with a label of the dynamic enhanced magnetic resonance image to obtain dynamic enhanced magnetic resonance volume data with the label; dividing the labeled dynamic enhanced magnetic resonance volume data into a support set and a query set, and constructing a space-time circulation attention classifier by using the support set and the query set; and optimizing the space-time circulation attention classifier by using an improved meta-learning strategy, and performing molecular subtype prediction through the space-time circulation attent