Multidimensional Dual Encoding Network For Liver Lesion Classification From Multi-Phase Magnetic Resonance Imaging.

Abstract

Liver cancer has a high mortality rate and is a serious threat to human life. The study of automated methods for analyzing liver cancer is very helpful to doctors in making a diagnosis. The existing methods tend to ignore the information correlation between multiple modalities of magnetic resonance imaging and do not design networks for multiple modalities and liver lesions. These methods are deficient in liver lesion classification and prediction performance, limiting development of the field. Therefore, we consider the information correlation between the multimodalities and design a multidimensional dual encoding network that can make full use of the information between the eight modalities to improve the classification and the prediction performance of liver lesions. It consists of a multidimensional information extraction, a dual encoder, and a classification structure. Firstly, a method for the application of multimodal data is designed, and the multidimensional information extraction module is used to extract two-dimensional (2D) and three-dimensional (3D) information from all modalities. Then, the dual encoder is used to improve feature extraction and pass multi-scale information to the classification structure. Finally, two differently connected networks were used to train the model for joint prediction, improving the final results. In this paper, a multiphase magnetic resonance imaging dataset containing 498 images was used for the experiments. The method was validated by ablation studies and comparisons with state-of-the-art (SOTA) methods, achieving balanced F1 scores, Cohen_Kappa, accuracy, and area under curve of 0.781, 0.731, 0.779, and 0.944, respectively.