Liver Tumor Detection Via A Multi-Scale Intermediate Multi-Modal Fusion Network On Mri Images

Automatic liver tumor detection can assist doctors to make effective treatments. However, how to utilize multi-modal images to improve detection performance is still challenging. Common solutions for using multi-modal images consist of early, inter-layer, and late fusion. They either do not fully consider the intermediate multi-modal feature interaction or have not put their focus on tumor detection. In this paper, we propose a novel multi-scale intermediate multi-modal fusion detection framework to achieve multi-modal liver tumor detection. Unlike early or late fusion, it maintains two branches of different modal information and introduces cross-modal feature interaction progressively, thus better leveraging the complementary information contained in multi-modalities. To further enhance the multi-modal context at all scales, we design a multi-modal enhanced feature pyramid. Extensive experiments on the collected liver tumor magnetic resonance imaging (MRI) dataset show that our framework outperforms other state-of-the-art detection approaches in the case of using multi-modal images.