Medical images often consist of multiple modalities, such as multimodal MRI images commonly used in diagnosing and studying brain tumors., and multimodal images provide rich complementary information. In the past, multimodal image segmentation usually directly added or connected modal features in the early or middle stage, which made it difficult to obtain the connection between modal features. In addition, there is a difference in information between modals and modals, and the previous method did not dealign modal features, which is likely to lead to reduced the effect of modal fusion. Thus, we propose a Multiscale dual dynamic feature fusion transformer (MdcFormer) model to explore the effects of multi-scale features, spatial and channel dynamic fusion and modal feature alignment on the segmentation effect of multimodal medical images. Utilizing a multi-encoder configuration and a single decoder, we gather characteristics from various modes at various levels and blend them in a dynamic manner across both spatial and channel domains. The proposed approach was evaluated using the BraTS2020 benchmark dataset. Empirical findings indicate that the model enhances the precision of segmentation.
Recommendation system aims to provide effective and personalized recommendation for users and solve the problem of information overload. However, the existing recommendation system lack of effective utilization of heterogeneous data and have the problem of information loss in the process of semantic information fusion. In this paper, we propose a Multi-scale Semantic Fusion Recommendation model (MSFRec) based on heterogeneous information networks to solve the problems above. First, we use heterogeneous graph and metapath to describe the complex semantic structure in the recommendation tasks. Then we divide the neighborhood guided by metapath into multiple layers and use the multi-layer interaction to capture multi-scale semantic information. Finally, we use a two-stage relational attention to guide the multi-scale semantic fusion. Extensive experiments show that MSFRec achieves competitive results in recommendation tasks.
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