Intelligent Diagnosis and Prediction of Pancreatic Cancer based on Unimodal and Multimodal Data

Pancreatic cancer is a fatal disease, which has become a difficult problem for the medical community due to its high mortality rate and the great challenge of early diagnosis. This review focuses on the frontier of diagnosis and prediction methods of pancreatic cancer in different data modalities, it introduces the pathological basis and clinical difficulties of pancreatic cancer, and then in-depth analyzes two core technical paths: the model based on one-dimensional clinical data, and the medical imaging data model subdivided into unimodal and multimodal imaging analysis. Although the models based on one-dimensional data show practical value in the field of risk stratification and prediction, the method of feature extraction and multimodal information fusion based on image data with the help of deep learning has important potential in improving the accuracy and automation of early diagnosis. This paper analyzes the shortcomings of current methods in data quality, model interpretability and generalization ability, and looks forward to future research directions.