A Cascade Multimodal Fine-Grained MRI Image Grading Network For Preoperative Microvascular Invasion In Hepatocellular Carcinoma

Xinyu Liu, Yong Yi, Ye Luo

Abstract: Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preoperative MVI grading is beneficial for patients recovery and survival. However, preoperative MVI grading is primarily accomplished through magnetic resonance imaging (MRI), which is challenging due to the heterogeneity of tumors and the characteristics of MVI. In this paper, we propose a cascade network that extracts fine-grained information from multimodal MRI images to assist in accurate MVI grading. We extract fine-grained features from different modalities and integrate them using an attention-based module called Multimodal Fine-Grained generator (M-FG) to obtain finegrained features from multimodal MRIs. Extensive experiments show our MVI grading network achieved an accuracy of 0.77, up to 10% improvement compared to the comparative methods, which validates that our method effectively utilizes fine-grained features from different modalities and improves performance of MVI grading. The codes are available at https://github.com/lxy-146/FG_MVInet