Accurate liver lesion diagnosis is crucial for effective treatment planning, with Magnetic Resonance Imaging (MRI) being a key diagnostic tool due to its ability to provide detailed anatomical and functional information. Despite its benefits, the manual analysis of 3D multi-phase MR images is challenging for radiologists due to the complexity of the data and the variability in lesion characteristics. To address this issue, in this paper, we propose a novel approach that integrates convolutional neural networks and self-attention mechanisms using the UniFormer framework. This method combines local and global feature extraction to enhance the accuracy of liver lesion classification. By leveraging pretrained weights from video tasks, the model performs better in identifying and classifying lesions than traditional methods. Extensive experiments with the LLD-MMRI2023 dataset, which includes multi-phase MR images for liver lesions, demonstrate significant advancements in diagnostic accuracy. This approach not only aids in automating the analysis process but also supports radiologists by reducing diagnostic errors and improving patient care. The research highlights the effectiveness of combining convolutional and self-attention mechanisms in medical image analysis and suggests promising avenues for future automated diagnostic systems.