Recently, deep-learning-based approaches have been widely applied in sEMG-based gesture recognition. While some existing methods have explored the integration of time-domain and frequency-domain features, many still face challenges in comprehensively leveraging the complementary information from both domains, often due to limitations in capturing multi-scale temporal dependencies or sub-optimal fusion strategies. To address these persistent limitations and further enhance accuracy and robustness, we propose the Multi-Scale Dual-Stream Fusion Network (MSDS-FusionNet), a novel deep learning framework that integrates temporal and frequency-domain features for enhanced gesture classification accuracy. MSDS-FusionNet introduces two key innovations: the Multi-Scale Mamba (MSM) modules, which extract multi-scale temporal features through parallel convolutions with varying kernel sizes and linear-time sequence modeling with selective state spaces, enabling the capture of temporal patterns at multiple scales and the modeling of both short-term and long-term dependencies; and the Bi-directional Attention Fusion Module (BAFM), which effectively combines temporal and frequency-domain features using bi-directional attention mechanisms to fuse complementary information and improve recognition accuracy dynamically. Extensive experiments on the NinaPro dataset demonstrate that MSDS-FusionNet outperforms state-of-the-art methods, achieving accuracy improvements of up to 2.41%, 2.46%, and 1.38% on the DB2, DB3, and DB4 datasets, respectively, with final accuracies of 90.15%, 72.32%, and 87.10%. This study presents a robust and flexible solution for sEMG-based gesture recognition, effectively addressing the complexities of recognizing intricate gestures and offering significant potential for applications in prosthetics, virtual reality, and assistive technologies. The code of this study is available at https://github.com/hdy6438/MSDS-FusionNet.

Lung cancer is the highest-mortality cancer with the largest number of patients in the world. Early screening and diagnosis of lung cancer by CT imaging is of great significance to improve the cure rate of lung cancer. CT signs mean the information of comprehensive manifestations of diseases at different pathological stages and levels. Automatic analysis of CT images outputs the locations and sizes of lesion regions which can help radiologists to make a credible diagnosis and effectively improve the speed and accuracy of clinical diagnosis. In this paper, we first review the domestic and foreign research progress of lung CT signs, summarize a generic structure for expressing the implementation process of existing methods, and systematically describe the signs research based on the traditional machine learning method and deep learning method. Furthermore, we provide a systematic summary and comparative analysis of the existing methods. Finally, we point out the challenges ahead and discuss the directions for improvement of future work, providing reference for scholars in related fields.

Objective. High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity; however, it remains costly and logistically challenging. If comparable volumes could be generated directly from widely available scalp electroencephalography (EEG), advanced neuroimaging would become significantly more accessible. Existing EEG-to-fMRI generators rely on plain convolutional neural networks that fail to capture cross-channel time–frequency cues or on heavy transformer/generative adversarial network decoders that strain memory and stability. Approach. To address these limitations, we propose Spec2VolCAMU-Net, a lightweight architecture featuring a Multi-directional Time–Frequency Convolutional Attention Encoder for rich feature extraction and a Vision-Mamba U-Net decoder that uses linear-time state-space blocks for efficient long-range spatial modeling. We frame the goal of this work as establishing a new state of the art in the spatial fidelity of single-volume reconstruction, a foundational prerequisite for the ultimate aim of generating temporally coherent fMRI time series. Main results. Trained end-to-end with a hybrid SSI-MSE loss, Spec2VolCAMU-Net achieves state-of-the-art fidelity on three public benchmarks, recording structural similarity index (SSIM) of 0.693 on NODDI, 0.725 on Oddball and 0.788 on CN-EPFL, representing improvements of 14.5%, 14.9%, and 16.9% respectively over previous best SSIM scores. Furthermore, it achieves competitive peak signal-to-noise ratio (PSNR) scores, particularly excelling on the CN-EPFL dataset with a 4.6% improvement over the previous best PSNR, thus striking a better balance in reconstruction quality. Significance. The proposed model is lightweight and efficient, making it suitable for real-time applications in clinical and research settings. The code is available at https://github.com/hdy6438/Spec2VolCAMU-Net .