Electrocardiogram (ECG) interpretation using deep learning models holds immense potential for improving cardiac diagnosis. However, existing models often suffer from overconfident predictions and lack the capability to directly quantify uncertainty, leading to unreliable clinical guidance. To address this challenge, we propose a model for uncertainty-aware ECG interpretation. The model employs a deep convolutional architecture with max-pooling residual modules to capture both local and global spatiotemporal features from raw ECG signals. The architectural design respects the symmetry inherent in ECG waveforms—such as periodicity and morphological consistency across cardiac cycles—enabling the network to extract clinically relevant features more effectively. Then, unlike conventional models that rely on softmax-based probability outputs, our approach parameterizes class distributions using the Dirichlet distribution, while Subjective Logic translates these parameters into interpretable belief masses and uncertainty scores. We evaluate the model on the PhysioNet Challenge 2017 dataset, our model achieves an accuracy of 86.12%, an F1 score of 83.14%, a Precision-Recall Area Under the Curve (PR-AUC) of 85.25%, and a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 92.87%—outperforming baseline models in three out of four metrics. Critically, the model reduces overconfidence to 0.59% (compared to 12–22% in softmax-based baselines), aligning prediction confidence with true accuracy. By progressively increasing the uncertainty threshold u, the model dynamically filters low-confidence predictions, leading to consistently improved performance—reaching up to 93.59% accuracy, 93.22% F1 score, 89.17% PR-AUC, and 95.10% ROC-AUC at u = 0.1. These results validate the model’s capacity for reliable ECG interpretation while leveraging physiological signal symmetry for enhanced feature extraction.

Attention mechanisms have been widely used to address the common multi-scale challenges in remote sensing imagery for tasks such as object detection and instance segmentation. However, they often require the design of task-specific network architectures tailored to different data targets. To better accommodate objects of varying scales and downstream task requirements, we propose a dynamic search framework based on a Mixture-of-Experts model. In this framework, each module is treated as an expert, and their combinations are flexibly adjusted to ensure that the pyramid structure adapts to the needs of different-scale tasks. Specifically, we design a Scalable Bi-Directional Feature Pyramid Network (SBFPN), which incorporates various hybrid attention mechanisms to dynamically enhance feature fusion across different layers. This approach not only captures long-range dependencies within the image but also suppresses noise and interference from complex backgrounds. The scalable feature pyramid structure we construct utilizes sparse feature fusion within the same layers, while also computing self-attention weights between nodes and retaining or trimming skip connections across layers. We conduct experiments on object detection, instance segmentation, and panoptic segmentation tasks. This structure can be transferred to YOLO-based and R-CNN-based networks, achieving superior detection and segmentation performance. On the Airbus Ship dataset, the mAP for detection and segmentation increased from 71.3% and 62.4% to 82.7% and 71.1%, respectively, demonstrating the effectiveness of our proposed method. Code is available at https://github.com/chaibosong/SAFPN.

Transferring cell type annotations from reference dataset to query dataset is a fundamental problem in AI-based single-cell data analysis. However, single-cell measurement techniques lead to domain gaps between multiple batches or datasets. The existing deep learning methods lack consideration on batch integration when learning reference annotations, which is a challenge for cell type annotation on multiple query batches. For cell representation, batch integration can not only eliminate the gaps between batches or datasets but also improve the heterogeneity of cell clusters. In this study, we proposed PLKD, a cell type annotation method based on pattern learning and knowledge distillation. PLKD consists of Teacher (Transformer) and Student (MLP). Teacher groups all input genes (features) into different gene sets (patterns), and each pattern represents a specific biological function. This design enables model to focus on biologically relevant functions interaction rather than gene-level expression that is susceptible to gaps of batches. In addition, knowledge distillation makes lightweight Student resistant to noise, allowing Student to infer quickly and robustly. Furthermore, PLKD supports multi-modal cell type annotation, multi-modal integration and other tasks. Benchmark experiments demonstrate that PLKD is able to achieve accurate and robust cell type annotation.