Pan-sharpening refers to fusing remote sensing multispectral (MS) and panchromatic (PAN) images to generate high-resolution multispectral (HR-MS) images. Recent advancements in deep learning-based pan-sharpening techniques have shown promising results. However, they face the following two issues. On one hand, there is a modality gap between MS and PAN images. Directly fusing them can lead to spectral and spatial distortions. On the other hand, the fusion process is prone to information loss, which can lead to image blurriness. To tackle these issues, we develop a Transformer-based model: FAFormer, which incorporates frequency analysis and focuses on the correlation and specificity of the PAN and MS images. Focusing on correlation can reduce the spectral and spatial distortions while focusing on specificity can reflect the specific information from MS and PAN images in the fusion result. We utilize the Discrete Wavelet Transform (DWT) to obtain the correlate and specific features. We introduce bijective functions based on the Transformer to design an Integrated Attention Block (IAB). As a critical component of the model, it effectively utilizes the correlation and specificity of the two images. In designing the model’s overall framework, we employ a Correlative Feature Attention Module (CFAM) to leverage the correlation between MS and PAN. We utilize a Specific Feature Attention Module (SFAM) to integrate specific information into fused features gradually. Experimental results show that our method improves pan-sharpening performance and has practical value. Codes are available at https://github.com/Xidian-AIGroup190726/FAFormer.

Despite various efforts to develop new predictive models for early detection of tumor local failure in locally advanced non-small cell lung cancer (NSCLC), many patients still suffer from a high local failure rate after radiotherapy. Based on recent studies of biomarker proteins' role in predicting tumor response following radiotherapy, we hypothesize that incorporation of physical and biological factors with a suitable framework could improve the overall prediction. To this end, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using a dataset of locally advanced NSCLC patients treated with radiotherapy. This dataset was collected prospectively, which consisted of physical variables and blood-based biomarkers. Our experimental results demonstrate that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables. The combined model of physical and biological factors outperformed individual physical and biological models, achieving an accuracy (acc) of 87.78%, Matthew's correlation coefficient (r) of 0.74, and Spearman's rank correlation coefficient (rs) of 0.75 on leave-one-out cross-validation analysis.