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MVI is a risk assessment factor related to hepatocellular carcinoma (HCC) recurrence after hepatectomy or liver transplantation. The goal of this paper is to study the preoperative diagnosis of microvascular invasion (MVI) by using a deep learning algorithm in non-contrast T2 weighted magnetic resonance imaging (MRI) images instead of pathological images. Herein, an ensemble learning algorithm named H-DARnet—based on the difference degree and attention mechanism, combined with radiomics, for MVI prediction—is proposed. Our hybrid network combines the fine-grained, high-level semantic, and radiomics features and exhibits a rich multilevel-feature architecture composed of global-local-prior knowledge with suitable complementarity. The total loss function comprises two regularization items––the triplet and the cross-entropy loss function––which are selected for the triplet network and SE-DenseNet, respectively. The hard triplet sample selection strategy for a triplet network and data augmentation for small-scale liver image datasets in convolutional neural network (CNN) training is indispensable. For 200 patch level test samples (135 positive samples and 65 negative samples), our method can obtain the best prediction results, the AUC, sensitivity, and specificity were 0.826, 79.5% and 73.8%, respectively. The experiment results show that MVI can be predicted by using MRI images, and the proposed method is better than other deep learning algorithms and hand-crafted feature algorithms. The proposed ensemble learning algorithm is proved to be an effective method for MVI prediction. • The goal of this paper is to study the preoperative diagnosis of HCC MVI by using a deep learning algorithm on T2WI. • An algorithm named H-DARnet based on the difference degree and attention mechanism, combined with radiomics is proposed. • Our hybrid network exhibits a rich multilevel-feature architecture composed of global-local-prior knowledge. • Hard triplet sample selection strategy and data augmentation for small-scale datasets in CNN training is indispensable. • For 200 patch level test samples, our method can obtain the AUC, sensitivity, and specificity 0.826, 79.5% and 73.8%. [ABSTRACT FROM AUTHOR]