Breast cancer is one of the most common cancers in women and has a relatively low mortality rate. Early detection is essential to improve survival rates. Currently, conventional techniques for breast cancer detection expose patients to harmful radiation and require breast compression, which is uncomfortable. Therefore, microwaves have emerged as a promising technique for early detection because they are safe and non-invasive, enabling early detection of malignancy. In this work, an ultra-wideband antenna is designed using high-frequency simulation software (HFSS). The backscattered signals from the antenna, when placed around the breast, are processed with a confocal imaging algorithm to locate the tumor. This study presents the imaging algorithm in detail, step by step. Additionally, a Support Vector Regression algorithm (SVR) is applied to the dataset obtained from the antenna to estimate detection accuracy. The SVR algorithm achieves a 95.94
This preliminary validation study investigated the feasibility of a portable biomedical device equipped with force and motion sensors for objectively quantifying resistance to passive movement in individuals with chronic post-stroke hemiparesis. The study examined associations between device-derived measurements and clinical spasticity scores obtained using the Modified Ashworth Scale (MAS). Twenty-one individuals with chronic post-stroke hemiparesis were evaluated. MAS grading and device-based measurements were performed simultaneously during the same passive stretch to ensure comparable biomechanical conditions. Synchronized resistance-force and angular-displacement data were recorded during assessments of the shoulder adductors, elbow flexors, and wrist flexors. Associations between device measurements and MAS scores were analyzed using Spearman's rank correlation. Strong positive correlations were observed between device-derived resistance measurements and MAS scores for the shoulder adductors (ρ = 0.79, p < 0.001) and elbow flexors (ρ = 0.80, p < 0.001). A moderate correlation was found for the wrist flexors (ρ = 0.47, p = 0.003). The findings provide preliminary evidence of concurrent associations between quantitative resistance measurements and MAS scores, supporting the device's feasibility as an adjunct tool for objective biomechanical assessment of post-stroke spasticity. However, larger studies incorporating independent blinded examiners, repeated measurements, and formal reliability analyses are necessary to establish its measurement properties and clinical applicability.
Compact YOLOv11 nano/small variants were trained on public cervical cytology datasets, harmonized via Bethesda labeling for cells and nuclei at high resolution. Models performed joint detection and segmentation, assessed by mAP, recall, and F1-score per category. Real-time use was tested on video from CMOS cameras on a microscope, tracking inference time and frame rate. Compact YOLOv11 nano and small variants were trained on public cervical cytology images. These datasets were aligned using a unified Bethesda labeling scheme for whole cells and nuclei at high resolution. The models were trained for joint box detection and segmentation, and their results were assessed via mAP, recall, and F1-score for each Bethesda category. The real-time use of the models was tested by deploying them on video streams from low-cost CMOS cameras on a microscope, recording the per-frame inference time and frame rate. The YOLOv11 small model achieved high mAP and recall for both bounding-box and mask predictions across Bethesda classes while maintaining low latency for real-time applications. Inference on microscope video sequences showed frame rates above the 25 fps threshold for real-time performance, even under tough conditions with overlapping cells and complex inflammatory or hemorrhagic backgrounds. These findings suggest that lightweight YOLOv11-based models can offer fast and reliable analysis of conventional Pap smears directly at the microscope, which can support the development of affordable, point-of-care computer-assisted screening tools for cervical cancer, notably in low- and middle-income countries where access to liquid-based cytology and whole-slide scanners is limited.
This study evaluates the influence of connectivity analysis parameters on the ability of weighted Phase Lag Index (wPLI)-based networks, derived from stereoelectroencephalography (SEEG) recordings, to represent the epileptogenic network. It also identifies the parameter combinations that most accurately delineate the epileptogenic zone. By comparing network metrics obtained under different connectivity configurations with surgical resections and postoperative outcomes, we aimed to establish a framework for selecting connectivity analysis parameters in epilepsy surgery planning. This optimized framework could improve the precision of stimulation zone identification, thereby refining resection or ablation strategies and potentially leading to better surgical outcomes and improved quality of life for patients with refractory epilepsy, while complementing clinical judgment. Ictal SEEG recordings from 28 patients with refractory epilepsy were analyzed. Functional networks were generated using wPLI, varying frequency bands, start/end analysis times, and epoch durations. Connectivity degree and betweenness centrality were then compared with surgical outcomes using the R coefficient and Mann-Whitney test. Optimal configurations showed significant overlap between resected nodes and key network nodes in patients with successful surgical outcomes. Both the theta (4–8 Hz) and high-frequency (160–220 Hz) bands were represented among the most effective configurations, most commonly combined with analysis windows beginning at or shortly before ictal onset; an epoch duration of 0.5 s was the most frequent choice among these configurations. Rather than assessing a single connectivity configuration, this study establishes a framework for selecting connectivity analysis parameters in ictal SEEG. The identified configurations generated network representations that were more consistent with surgically validated epileptogenic regions, providing methodological guidance for future network-based surgical planning.
Medical imaging has been transformed by Artificial Intelligence (AI) and Deep Learning (DL). Yet, multi-hospital deployment remains limited by patient privacy concerns, heterogeneous data distributions, and insufficient model interpretability, which affect regulatory approval and clinical trust. This study proposes a regulatory-grade Federated Learning (FL) framework for secure, interpretable, and generalizable collaborative medical imaging. The proposed framework integrates Slicing Window Adaptive Kalman Filtering (SWAKF) for image denoising, Structured Multi-Modal Autoencoder Attention Fusion (SMAAF) for feature representation, and adaptive federated aggregation to address non-IID data across hospitals. Patient privacy is preserved using secure aggregation, differential privacy, and encryption, while Grad-CAM, SHAP, and LIME provide model interpretability. The proposed framework outperformed Vision Transformer, AlexNet, FedAvg, and FedProx on Brain Tumor and Alzheimer's MRI datasets. It achieved 96.1
