CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation.

Deep learning-based methods have recently shown great promise in medical image segmentation task. However, CNN-based frameworks struggle with inadequate long-range spatial dependency capture, whereas Transformers suffer from computational inefficiency and necessitate substantial volumes of labeled data for effective training. To tackle these issues, this paper introduces CI-UNet, a novel architecture that utilizes ConvNeXt as its encoder, amalgamating the computational efficiency and feature extraction capabilities.

Effect of Al-5Ti-0.25C-0.25B and Al-5Ti-1B Master Alloys on the Microstructure and Mechanical Properties of Al-9.5Si-1.5Cu-0.8Mn-0.6Mg Alloy.

The goal of this research was to determine how the master alloys Al-5Ti-0.25C-0.25B and Al-5Ti-1B affected the mechanical properties and structural characteristics of the alloy Al-9.