Multi-model fusion pancreatic cystic lesion feature selection method and system

Abstract

This invention proposes a multi-model fusion method and system for feature selection in pancreatic cystic lesions, belonging to the field of medical feature selection technology. This invention proposes a multi-modal feature fusion and screening method for the diagnosis of pancreatic cystic lesions. Based on a medical knowledge base, it dynamically fuses pathological imaging and biochemical data to generate a fully coupled feature vector. Multi-modal features are screened according to guideline co-occurrence rules to ensure clinical consistency. A two-stream neural network is used to calculate feature importance scores, combined with medical thresholds for feature screening. Furthermore, the stability of the IPMN clinical rules is statistically analyzed, and a threshold is set to determine the final feature set. Through the technical solution of this application, multi-modal deep coupling between feature screening and pathological mechanisms is achieved, significantly improving the sensitivity of subtype diagnosis and providing a highly reliable basis for precision diagnosis and treatment.

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