Due to the limitation of local characteristics of convolution operations, the encoder of U-Net cannot effectively capture global context information; furthermore, the skip connections of U-Net fail to capture salient features in image segmentation tasks. Therefore, we put forward a Saliency fast Fourier convolutional Network (SFNet) for medical image segmentation. To begin with, we propose a SCAU attention module, which can highlight both spatial and channel attention for capturing not only global but local information, paying more attention to the whole target regions of an image, and creating strong information association among samples to extract the characteristics of the overall dataset. Subsequently, instead of employing the convolution to set the encoder and decoder, we introduce a Fourier convolution module, FFconv, which owns the non-local receptive field to fulfil cross-scale fusion in the convolution unit properly. Experimental results show that, on BUSI and Kvasir-SEG datasets, the mIOU and F1-score of our network reach 75.08% and 84.75%, respectively; our network greatly promote medical image segmentation performance.

Accuracy segmentation of COVID-19 lesions in lung CT images can aid patient screening and diagnosis. However, the blurred, inconsistent shape and location of the lesion area poses a great challenge to this vision task. To tackle this issue, we propose a multi-scale representation learning network (MRL-Net) that integrates CNN with Transformer via two bridge unit: Dual Multi-interaction Attention (DMA) and Dual Boundary Attention (DBA). First, to obtain multi-scale local detailed feature and global contextual information, we combine low-level geometric information and high-level semantic features extracted by CNN and Transformer, respectively. Secondly, for enhanced feature representation, DMA is proposed to fuse the local detailed feature of CNN and the global context information of Transformer. Finally, DBA makes our network focus on the boundary features of the lesion, further enhancing the representational learning. Amounts of experimental results show that MRL-Net is superior to current state-of-the-art methods and achieves better COVID-19 image segmentation performance.

While many existing multi-task learning based Wi-Fi location approaches pay more attention on the location performance, they generally neglect determining key access points(APs). In order to reduce maintenance cost in complex indoor environment, a new multi-task learning based Wi-Fi location approach is proposed to find the key APs with enough accuracy. First, we introduce extreme learning machine as basic method to establish a new multi-task learning machine. This machine is based on the assumption that the hypotheses learned from a latent feature space, rather than the original high-dimensional feature space, are similar, in which L1/2-iiorm is utilized to construct L2-1/2-norm to achieve joint feature selection in multi-task scenario. An alternating optimization method is employed to solve this problem, by iteratively optimizing the latent space and key features. Experiments on real-world indoor localization data are conducted, and the results demonstrate the effectiveness of the proposed approach.

In this letter, the graph-based visual saliency (GBVS) model is extended by pulse-coupled neural network (PCNN) to implement the well-defined criteria for a saliency detector. In receptive field, the resized intensity feature map generated by GBVS was regarded as the input image of the PCNN. After modulation, the optimal iteration number and threshold were identified by GBVS and Otsu's method in pulse generator part, respectively. Moreover, other parameters of the PCNN were set automatically. In the end, an automatic salient region detection algorithm was proposed. Experimental results show that our proposed hybrid model can efficiently detect salient region.