Recent advances in multi-view fundus imaging show great promise for automated diabetic retinopathy (DR) grading. However, mainstream end-to-end CNN/Transformer pipelines rely on striding or tokenization that compresses spatial detail, causing small, low-contrast lesions (e.g., microaneurysms) to be under-represented and creating performance ceilings. Prior efforts have mitigated this by incorporating external lesion- or vessel-level annotations into models. However, such labels are costly to acquire, break the end-to-end training, and make performance over-reliant on the annotation quality. To reduce dependence on expensive annotations, we propose an end-to-end framework that generates lesion proposals on the fly during training and inference, providing self-derived cues for grading. First, we introduce a Grade-Activated Lesion Proposal (GALP) module that derives grade-conditioned evidence maps (GEMs) from stage-wise auxiliary classifiers and selects the top-K high-evidence regions per view as lesion proposals. Second, we propose a Cross-View Lesion Expert Guided Regional Fusion (LGRF) module, which selectively activates experts for a view's lesion proposals based on contextual guidance from other views, ensuring that only the most relevant feature extractors contribute to fusion. Experimental results on two multi-view DR datasets show that our method matches or surpasses strong baselines without external annotations, confirming that self-generated proposals can substantially reduce annotation needs.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-art performance on the largest clean dataset, PulseDB. The rU-Net architecture, a fusion of U-Net and ResNet, enhances both generalization and feature extraction accuracy. Accurate multi-scale feature capture is facilitated by short-time Fourier transform (STFT) time-frequency distributions and multi-head attention mechanisms, allowing data-driven feature selection. The inclusion of demographic parameters as supervisory information further elevates performance. On the calibration-based dataset, our model excels, achieving outstanding accuracy (SBP MAE ± std: 4.49 ± 4.86 mmHg, DBP MAE ± std: 2.69 ± 3.10 mmHg), surpassing AAMI standards and earning a BHS Grade A rating. Addressing the challenge of calibration-free data, we propose a fine-tuning-based transfer learning approach. Remarkably, with only 10% data transfer, our model attains exceptional accuracy (SBP MAE ± std: 4.14 ± 5.01 mmHg, DBP MAE ± std: 2.48 ± 2.93 mmHg). This study sets the stage for the development of highly accurate and reliable wearable cuffless blood pressure monitoring devices.

Continuous monitoring of the blood glucose levels is essential and critical for controlling diabetes and its complications. With the improvement of the measurement accuracy of the acquisition devices developed in recent decades, developing the optical‐based methods for performing the non‐invasive blood glucose estimation for the consumer applications becomes very important. The authors’ previous work is based on the heart rate variability of the electrocardiogram and the existing method is based on applying the random forest to the features extracted from the photoplethysmogram. However, the accuracies of these two methods are not very high. In this study, a joint empirical mode decomposition and exponential function estimation approach is proposed for estimating the mean value of a photoplethysmogram acquired from a wearable non‐invasive blood glucose device. Also, the exponential function fitting approach is employed for estimating the blood glucose levels via an L 1 norm formulation. The computer numerical simulation results show that the estimation accuracy based on their proposed method is higher than that based on the state‐of‐the‐art methods. Therefore, their proposed method can be employed for performing blood glucose estimation effectively.