Super-Resolution and Self-Attention with Generative Adversarial Network for Improving Malignancy Characterization of Hepatocellular Carcinoma

The slice thickness of MR imaging may remarkably degrade the clarity of 3D lesion images within through-plane slices (coronal or sagittal views) so as to influence the performance of lesion characterization. To alleviate the problem, we propose an end-to-end super-resolution and self-attention framework based on Generative adversarial networks (GAN) for improving the malignancy characterization of hepatocellular carcinoma (HCC). Specifically, a super-resolution subnetwork is designed to enhance the low-resolution patches of coronal or sagittal views based on the high resolution patches of the axial view, and then the enhanced patches are fed into the classification subnetwork for malignancy characterization. Furthermore, a self-attention mechanism is utilized to extract multi-level features for better super-resolution and lesion characterization. Experimental results of clinical HCCs demonstrate the superior performance of the proposed method compared with conventional CNN-based methods and show the potential in clinical practice.