The pathological grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC) are two key factors related to the patient's prognosis. Previous studies usually predict these two factors separately based on medical images. In this study, we propose an end-to-end multi-task deep learning network to simultaneously predict the MVI and grading information. Specifically, we are the first to demonstrate that these two tasks are related and can promote each other in the framework of multi-task deep learning. Experimental results of HCC in Contrast-enhanced MR demonstrate the effectiveness of the proposed method, outperforming the single task learning.
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