Predicting Symptoms from Multiphasic MRI via Multi-instance Attention Learning for Hepatocellular Carcinoma Grading

Liver cancer is the third leading cause of cancer death in the world, where the hepatocellular carcinoma (HCC) is the most common case in primary liver cancer. In general diagnosis, accurate prediction of HCC grades is of great help to the subsequent treatment to improve the survival rate. Rather than to straightly predict HCC grades from images, it will be more interpretable in clinic to first predict the symptoms and then obtain the HCC grades from the Liver Imaging Reporting and Data System (LI-RADS). Accordingly, we propose a two-stage method for automatically predicting HCC grades according to multiphasic magnetic resonance imaging (MRI). The first stage uses multi-instance learning (MIL) to classify the LI-RADS symptoms while the second stage resorts LI-RADS to grade from the predicted symptoms. Since our method provides more diagnostic basis besides the grading results, it is more interpretable and closer to the clinical process. Experimental results on a dataset with 439 patients indicate that our two-stage method is more accurate than the straight HCC grading approach.