To solve the problem that the interference between millimeter wave radars will cause the weak vital sign signal obtained by bioradar to be submerged, resulting in the inability to accurately measure respiration and heartbeat, a method is proposed based on improved singular spectrum analysis to suppress the interference between radars, and the target beat signal is reconstructed from the interfered signal through correlation calculation to suppress the interference and eliminate the background noise. Furthermore, an ensemble empirical mode decomposition method based on information entropy is proposed to eliminate the residual phase noise of the beat signals, and the respiration and heartbeat signals are selected from the intrinsic mode function components after ensemble empirical mode decomposition through information entropy calculation to suppress the residual noise. Experimental results show that the proposed method can effectively recover the respiration and heartbeat signals from the interfered signals, and improve the signal-to-noise ratios of respiration and heartbeat. Therefore, the methods proposed in this research improve the anti-interference ability of bioradar and enhance the practicability of bioradar.

To address the issue that the presence of dynamic clutter in actual environments will affect the accuracy of human target localization and vital signs detection, a coarse-to-fine point cloud selection strategy and an adaptive variational modal decomposition method based on the quality factor are proposed to achieve the suppression of dynamic clutter and the enhancement of vital signs detection performance. First, coarse point clouds of the human body and dynamic objects are distinguished by autocorrelation analysis. Second, a spectrum-based multi-feature fusion model is proposed to select fine point cloud with strong vital signs. Third, a quality factor-based variational mode decomposition method is proposed to separate the dynamic clutter and weak vital signals. Finally, a harmonic weighting selection algorithm is proposed to adaptively extract the respiratory and heartbeat components. Experiments conducted in cluttered indoor environments show that the proposed method effectively mitigates the effects of dynamic clutter and achieves accurate detection of human vital signs in dynamic environments, achieving respiratory and heart rate accuracies of 98.01% and 98.14%, respectively.

Accurate diagnosis and subtyping of non-small cell lung cancer (NSCLC) are crucial for providing patient-specific precision treatment. However, the inherent tumor heterogeneity of NSCLC leads to significant morphological variations within the same subtype and similarities across different subtypes, presenting substantial challenges for pathologists. To address this issue, this study proposes a novel computer-aided diagnostic framework that integrates multi-scale feature extraction and fusion through multi-instance deep learning. The proposed method aims to effectively leverage the heterogeneous information presented in pathological whole-slide images (WSIs) to improve the accuracy of NSCLC subtype classification. Initially, the framework performs multi-scale sampling and feature extraction from WSIs at various levels, such as cellular and tissue levels, to capture both local and global contextual information. Subsequently, a vision transformer network is employed to model the complex dependencies among instances of varying granularity, facilitating end-to-end fusion of the extracted features for accurate classification. Furthermore, we introduce an attention-based instance loss function that adaptively weighs the contribution of each instance based on its discriminative power, providing additional supervision to enhance the classification performance of the model. We evaluat our method on a large public dataset containing 1 674 H&E-stained pathological slide images of NSCLC. The experimental results demonstrate that our multi-scale fusion method effectively leverages the rich information in multi-grained pathological data, significantly outperforming single-scale approaches in NSCLC subtype classification accuracy. Moreover, the method's attention heatmaps offer interpretability and allow for intuitive assessment of individual sample classification quality, serving as a quantitative analytical tool for further model refinement and validation. In conclusion, the proposed multi-scale multi-instance learning framework provides a powerful and interpretable solution for accurate NSCLC subtype classification, which has the potential to assist pathologists in making more reliable diagnostic decisions and ultimately improve patient care.