Hepatocellular carcinoma patients after thermal ablation suffer high recurrence rate. In this study, we proposed a deep learning method to predict the early recurrence in these patients. Compared with other predictive models, two innovations were achieved in our study: 1) integrating interconnected tasks, i.e., tumor segmentation and tumor progression prediction, into a unified model to perform co-optimization; 2) using longitudinal images to take the therapy-induced changes into consideration to explore the temporal information. Results showed that our approach can simultaneously perform tumor segmentation and tumor progression prediction with higher performance than only doing any single one of them.
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