TransLiver: A Hybrid Transformer Model for Multi-phase Liver Lesion Classification

Early diagnosis of focal liver lesions (FLLs) can decrease the fatality rate of liver cancer, which remains a big challenge. We designed a deep learning approach based on CT to assess and differentiate FLLs. To achieve high accuracy, CTs in different phases are integrated to provide more information than single-phase images. While most of the related studies use convolutional neural networks, we exploit the Transformer for multi-phase liver lesion classification. We propose a hybrid model called TransLiver, which has a transformer backbone and complementary convolutional modules. Specifically, we connect modified transformer blocks with convolutional encoder and down-samplers. For multi-phase fusion, we utilize cross phase tokens to reinforce the phases communication. In addition, we introduce a pre-processing unit to resolve realistic annotation issues. Extensive experiments are conducted, in which we achieve an overall accuracy of 90.9% on an in-house dataset of four CT phases and seven liver lesion classes. The results also show distinct advantages in comparison to state-of-art approaches in classification. The code is available at https://github.com/sherrydoge/TransLiver .