U-Net and its variants have achieved impressive results in medical image segmentation. However, the downsampling operation of such U-shaped networks causes the feature maps to lose a certain degree of spatial information, and most existing methods use convolution and transformer sequentially, it is hard to extract more comprehensive feature representation of the image. In this paper, we propose a novel U-shaped segmentation network named Multi-scale Axial Attention Network (MSAANet) to solve the above problems. Specifically, we propose a cross-scale interactive attention: multi-scale axial attention (MSAA), which achieves direction-perception attention of different scales interaction. So that the downsampling deep features and the shallow features can maintain context spatial consistency. Besides, we propose a Convolution-Transformer (CT) block, which makes transformer and convolution complement each other to enhance comprehensive feature representation. We evaluate the proposed method on the public datasets Synapse and ACDC. Experimental results demonstrate that MSAANet effectively improves segmentation accuracy.

Recently, several Transformer-based methods have been presented to improve image segmentation. However, since Transformer needs regular square images and has difficulty in obtaining local feature information, the performance of image segmentation is seriously affected. In this paper, we propose a novel encoder-decoder network named TCRNet, which makes Transformer, Convolutional neural network (CNN) and Recurrent neural network (RNN) complement each other. In the encoder, we extract and concatenate the feature maps from Transformer and CNN to effectively capture global and local feature information of images. Then in the decoder, we utilize convolutional RNN in the proposed recurrent decoding unit to refine the feature maps from the decoder for finer prediction. Experimental results on three medical datasets demonstrate that TCRNet effectively improves the segmentation precision.

Cross-database classification means that the model is able to apply to the serious disequilibrium of data distributions, and it is trained by one database while tested by another database. Thus, cross-database pneumonia detection is a challenging task. In this paper, we proposed a new framework based on transfer learning for cross-database pneumonia detection. First, based on transfer learning, we fine-tune a backbone that pre-trained on non-medical data by using a small amount of pneumonia images, which improves the detection performance on homogeneous dataset. Then in order to make the fine-tuned model applicable to cross-database classification, the adaptation layer combined with a self-learning strategy is proposed to retrain the model. The adaptation layer is to make the heterogeneous data distributions approximate and the self-learning strategy helps to tweak the model by generating pseudo-labels. Experiments on three pneumonia databases show that our proposed model completes the cross-database detection of pneumonia and shows good performance.

With the growing needs of practical applications such as security monitoring, partial face recognition is a challenging but important issue, because the captured faces in real-world surveillance videos may be occluded or with variations. Though current face recognition methods perform well in relatively constrained scenes, they may suffer from degradation for partial faces. In this paper, we propose a framework of model-based transfer learning and sparse coding (MTLSC) for partial face recognition. First, due to less information in partial face image, we exploit the mirrored image of an original probe sample as sample augment to provide further information. Considering the inadequacy of training face samples, we obtain face features based on model-based transfer learning VGGNet that is pre-trained on VGGFace dataset. Then we reconstruct face features by sliding window in view of different sizes of partial face hard to extract the same feature dimension. Finally we carry out sparse coding with rectification and calculate the minimum score of the probe and mirrored samples among all classes to get the results. Thus, by model-based transfer learning, sliding window for feature reconstruction and sparse coding with rectification, the proposed framework improves partial face recognition performance. Experimental results on three face databases (LFW, AR and NIR), and two person re-identification databases (iLIDS-VID and PKU-Reid) demonstrate our method is effective for partial face recognition.