Alzheimer’s Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. Early diagnosis is challenging due to subtle symptoms and varied presentations, often leading to misdiagnosis with traditional unimodal diagnostic methods due to their limited scope. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, and EEG data to enhance diagnostic accuracy. The model incorporates a feature tagger with a tabular data coding architecture and utilizes the TimesBlock module to capture intricate temporal patterns in Electroencephalograms (EEG) data. By employing Cross-modal Attention Aggregation module, the model effectively fuses Magnetic Resonance Imaging (MRI) spatial information with EEG temporal data, significantly improving the distinction between AD, Mild Cognitive Impairment, and Normal Cognition. Simultaneously, we have constructed the first AD classification dataset that includes three modalities: EEG, MRI, and tabular data. Our innovative approach aims to facilitate early diagnosis and intervention, potentially slowing the progression of AD. The source code and our private ADMC dataset are available at https://github.com/JustlfC03/MSTNet.

Infrared small target detection (IRSTD) has been widely applied and developed in military and civilian fields, playing a vital role. Despite the extensive research foundation of traditional manual feature-based methods, they are still constrained by the inherent problem of infrared small targets lacking prior features. In recent years, the advancement of deep learning methods has enriched the research landscape in this field, yet they are still constrained by the imbalance of positive and negative samples between the target and the background. To address these issues, we propose a novel prior guided dense nested network (PGDN-Net), which ingeniously integrates traditional manual features with a deep learning network model. First, three prior features are extracted, including the high-order Riesz transform feature, the compactness and heterogeneity feature (CH), and the corner feature of the structure tensor (ST). Then, these features are input into a dense nested network for guidance, supported by a two-orientation attention aggregation module and a channel and spatial attention module. Different features play their respective guiding roles in different depths of the network. Through multiple attention mechanisms and feature fusion operations on the interested target area, the extraction and preservation of target features can be improved, while easily removing irrelevant backgrounds. Experiments on public datasets demonstrate the effectiveness and progressiveness of our PGDN-Net. Compared with other state-of-the-art methods, it achieves better performance in background suppression, target enhancement, probability of detection, and false alarm rate. In addition, the PGDN-Net model can effectively maintain and restore the original shape of the target while performing robust detection, which is beneficial for subsequent fine-grained recognition tasks.

Robust detection of infrared small target under complex background is of great significance for infrared search and tracking applications. However, the inherent problem of limited prior features for infrared small target has always made its detection task a challenging research topic. In order to solve the problem, we propose a novel infrared small target detection method based on monogenic signal decomposition and feature expansion, which can effectively enrich and extract the potential features of the target. First, a series of local information of the original image is obtained through the monogenic signal constructed by Riesz transform. Then, various features of the small target are extracted from different local signals, including the direction feature, edge feature, and local saliency feature. Finally, the fusion of target features is completed through signal reconstruction, thereby achieving target detection. This method not only pays attention to the local salient characteristic of the target, but also supplements the consideration of other characteristics of the target, providing a new idea for small target detection. The experimental results on real infrared images show that the proposed method framework is reasonable and effective, and possesses better detection performance and good generalization compared to other state-of-the-art methods.

Stitched remote sensing images often exhibit irregular boundaries, which can be frustrating for general users and detrimental to downstream tasks such as object detection and segmentation. However, this issue has received insufficient attention and remains unexplored within the remote sensing domain. In this study, we investigate mesh-based rectangling techniques for remote sensing images, aiming to produce rectangular outputs while preserving the original field-of-view (FoV) and avoiding the introduction of unreliable content. Observing that prior rectangling algorithms tend to generate unsatisfactory boundaries or discernible distortions, that is, under-rectangling or over-rectangling, we propose the concept of a warping kernel associated with mesh deformations to account for these phenomena. Consequently, we introduce the iterative warping kernel self-correction transformer (IWKFormer), designed to enhance warping kernel estimation and generate superior rectangular outcomes. It primarily comprises two components: a mesh feature extractor built upon the partial swin transformer block (PSTB) and a corrector module using the swin transformer block (STB). These modules collaborate to derive warping kernels implicitly. The extractor extracts latent features pertinent to mesh deformation, whereas the corrector iteratively refines the warping kernel estimation to improve the ultimate prediction. Furthermore, to bolster further research, we have constructed an aerial imagery stitching rectangling dataset (AIRD), featuring a wide array of stitching scenes. Extensive experimentation on the AIRD demonstrates that our method yields visually appealing and naturally rectangled images, achieving state-of-the-art performance. The code and data will be available athttps://github.com/yyywxk/IWKFormer.