The fusion of infrared and visible images aims to generate a composite image that can simultaneously contain the thermal radiation information of an infrared image and the plentiful texture details of a visible image to detect targets under various weather conditions with a high spatial resolution of scenes. Previous deep fusion models were generally based on convolutional operations, resulting in a limited ability to represent long-range context information. In this paper, we propose a novel end-to-end model for infrared and visible image fusion via a dual attention Transformer termed DATFuse. To accurately examine the significant areas of the source images, a dual attention residual module (DARM) is designed for important feature extraction. To further model long-range dependencies, a Transformer module (TRM) is devised for global complementary information preservation. Moreover, a loss function that consists of three terms, namely, pixel loss, gradient loss, and structural loss, is designed to train the proposed model in an unsupervised manner. This can avoid manually designing complicated activity-level measurement and fusion strategies in traditional image fusion methods. Extensive experiments on public datasets reveal that our DATFuse outperforms other representative state-of-the-art approaches in both qualitative and quantitative assessments. The proposed model is also extended to address other infrared and visible image fusion tasks without fine-tuning, and the promising results demonstrate that it has good generalization ability. The source code is available at https://github.com/tthinking/DATFuse.

Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that simultaneously contains functional metabolic information and structural tissue details. Multimodal medical image fusion, an effective way to merge the complementary information in different modalities, has become a significant technique to facilitate clinical diagnosis and surgical navigation. With powerful feature representation ability, deep learning (DL)-based methods have improved such fusion results but still have not achieved satisfactory performance. Specifically, existing DL-based methods generally depend on convolutional operations, which can well extract local patterns but have limited capability in preserving global context information. To compensate for this defect and achieve accurate fusion, we propose a novel unsupervised method to fuse multimodal medical images via a multiscale adaptive Transformer termed MATR. In the proposed method, instead of directly employing vanilla convolution, we introduce an adaptive convolution for adaptively modulating the convolutional kernel based on the global complementary context. To further model long-range dependencies, an adaptive Transformer is employed to enhance the global semantic extraction capability. Our network architecture is designed in a multiscale fashion so that useful multimodal information can be adequately acquired from the perspective of different scales. Moreover, an objective function composed of a structural loss and a region mutual information loss is devised to construct constraints for information preservation at both the structural-level and the feature-level. Extensive experiments on a mainstream database demonstrate that the proposed method outperforms other representative and state-of-the-art methods in terms of both visual quality and quantitative evaluation. We also extend the proposed method to address other biomedical image fusion issues, and the pleasing fusion results illustrate that MATR has good generalization capability. The code of the proposed method is available at https://github.com/tthinking/MATR .

Infrared and visible image fusion is aims to generate a composite image that can simultaneously describe the salient target in the infrared image and texture details in the visible image of the same scene. Since deep learning (DL) exhibits great feature extraction ability in computer vision tasks, it has also been widely employed in handling infrared and visible image fusion issue. However, the existing DL-based methods generally extract complementary information from source images through convolutional operations, which results in limited preservation of global features. To this end, we propose a novel infrared and visible image fusion method, i.e., the Y-shape dynamic Trans- former (YDTR). Specifically, a dynamic Transformer module (DTRM) is designed to acquire not only the local features but also the significant context information. Furthermore, the proposed network is devised in a Y-shape to comprehensively maintain the thermal radiation information from the infrared image and scene details from the visible image. Considering the specific information provided by the source images, we design a loss function that consists of two terms to improve fusion quality: a structural similarity (SSIM) term and a spatial frequency (SF) term. Extensive experiments on mainstream datasets illustrate that the proposed method outperforms both classical and state- of-the-art approaches in both qualitative and quantitative assess- ments. We further extend the YDTR to address other infrared and RGB-visible images and multi-focus images without fine- tuning, and the satisfactory fusion results demonstrate that the proposed method has good generalization capability. Our code is available at https://github.com/tthinking/YDTR.

With the rapid development of service-oriented computing (SOC)/service-oriented architecture (SOA), cloud computing and web services, cloud-based design and manufacture (CBDM) is emerging as state-of-the-art technologies and methodologies to enable collaborative product development (CPD). CBDM-enabled CPD can provide cost-effective, flexible and scalable solutions to collaborative partners by sharing the resources in the applications of design and manufacturing. Feature-based data exchange (FBDE) has been one of the key issues in history of CPD and should be adapted in lasted CBDM-enabled CPD. Firstly this paper presents a service-oriented architecture for data exchange in CBDM. Within this architecture, FBDE was registered as service and FBDE users in the CBDM environment can acquire a set of FBDE services to replace the traditional FBDE functions among heterogeneous CAD systems. Secondly, in orderto put the philosophy of FBDE-as-a-Service into practice for CBDM, this paper proposes a peerto peer (P2P) approach for service-oriented FBDE, which revolutionizes the traditional centralized and neutral-file based approach. Thirdly, technique issues of FBDE-as-a-Service in P2P architecture are discussed in details, including constituting of the P2P FBDE service, procedure of service-oriented P2P FBDE, pre-P2P FBDE service, topological entity matching between pre/post-P2P service and post-P2P FBDE service. Finally, a case study of data exchange is tested to demonstrate the proposed idea of service-oriented FBDE for CBDM.

In many microarray studies, classifiers have been constructed based on gene signatures to predict clinical outcomes for various cancer sufferers. However, signatures originating from different studies often suffer from poor robustness when used in the classification of datasets independent from which they were generated from. In this paper, we present an unsupervised feature learning framework by integrating a principal component analysis algorithm and autoencoder neural network to identify different characteristics from gene expression profiles. As the foundation for the obtained features, an ensemble classifier based on the AdaBoost algorithm (PCA-AE-Ada) was constructed to predict clinical outcomes in breast cancer. During the experiments, we established an additional classifier with the same classifier learning strategy (PCA-Ada) in order to perform as a baseline to the proposed method, where the only difference is the training inputs. The AUC (area under the receiver operating characteristic curve) index, MCC (Matthews correlation coefficient) index, ACC (accuracy), and other evaluation parameters of the proposed method were tested on several independent breast cancer datasets and compared with representative gene signature-based algorithms including the baseline method. Experimental results demonstrate that the proposed method using deep learning techniques performs better than others.