Diagnosing stages of osteonecrosis of the femoral head (ONFH) based on MR images can reduce additional cost and radiation exposure caused by CT scan. We propose a deep learning network that enables 4-way classification of ONFH stages, utilizing the information from MR images with different contrasts and planes. Given the limited number of available data, we enhanced the network performance by using self-supervised learning based on MR-to-CT translation task, which increased AUC significantly. We also investigated the diagnostic results from different MR protocols, and obtained more precise and robust results by combining them.
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