MFC-ACL: Multi-view fusion clustering with attentive contrastive learning.

Multi-view clustering can better handle high-dimensional data by combining information from multiple views, which is important in big data mining. However, the existing models which simply perform feature fusion after feature extraction for individual views, mostly fails to capture the holistic attribute information of multi-view data due to ignoring the significant disparities among views, which seriously affects the performance of multi-view clustering. In this paper, inspired by the attention mechanism, an approach called Multi-View Fusion Clustering with Attentive Contrastive Learning (MFC-ACL) is proposed to tackle these issues.

LTMSegnet: Lightweight multi-scale medical image segmentation combining Transformer and MLP.

Medical image segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. In recent years, neural network-based methods have improved in terms of segmentation accuracy and become the mainstream in the field of medical image segmentation. However, the large number of parameters and computations of prevailing methods currently pose big challenges when employed on mobile devices.