Rui Wu Xu

Brain tumour segmentation (BTS) is crucial for diagnosis and treatment planning by delineating tumour boundaries and subregions in multi‐modality bio‐imaging data. Several BTS models have been proposed to address specific technical challenges encountered in this field. However, accurately capturing intricate tumour structures and boundaries remains a difficult task. To overcome this challenge, HAB‐Net, a model that combines the strengths of convolutional neural networks and transformer architectures, is presented. HAB‐Net incorporates a custom‐designed hierarchical and pseudo‐convolutional module called hierarchical asymmetric convolutions (HAC). In the encoder, a coordinate attention is included to extract feature maps. Additionally, swin transformer, which has a self‐attention mechanism, is integrated to effectively capture long‐range relationships. Moreover, the decoder is enhanced with a boundary attention module (BAM) to improve boundary information and overall segmentation performance. Extensive evaluations conducted on the BraTS2018 and BraTS2021 datasets demonstrate significant improvements in segmentation accuracy for tumour regions.

The rapid development of artificial intelligence technology has changed the quadruped robots’ controlling system of gait from a traditional way into intelligent monitoring. At the beginning of this paper, analysis is conducted on several prominent robots developed by international groups. The gait research of quadruped robots includes an investigation of gait features and a comparison between different gait types, along with the analyses of the characteristics and the rationals behind different gaits in various applications. Hence, this paper summarised the progress of quadruped robots in gait research and the prospects are concluded to give ideas on how the future gait research on quadruped robots would proceed.