Event cameras, which capture pixel-level brightness changes asynchronously, provide rich motion information that is often missed during traditional frame-based camera exposures, thereby offering fresh perspectives for motion deblurring. Although current approaches incorporate event intensity, they neglect essential spatial motion information. Unlike their CNN architectures, Transformers excel in modeling long-range dependencies but struggle with establishing relevant non-local connections in sparse events and fail to highlight significant interactions in dense images. To address these limitations, we introduce a Motion-Adaptive Transformer network (MAT) that utilizes spatial motion information to forge robust global connections. The core design is an Adaptive Motion Mask Predictor (AMMP) that identifies key motion regions, guiding the Motion-Sparse Attention (MSA) to eliminate irrelevant event tokens and enabling the Motion-Aware Attention (MAA) to focus on relevant ones, thereby enhancing long-range dependency modeling. Additionally, we elaborately design a Cross-Modal Intensity Gating mechanism that efficiently merges intensity data across modalities while minimizing parameter use. The learnable Expansion-Controlled Spatial Gating further optimizes the transmission of event features. Comprehensive testing confirms that our approach sets a new benchmark in image deblurring, surpassing previous methods by up to 0.60dB on the GoPro dataset, 1.04dB on the HS-ERGB dataset, and achieving an average improvement of 0.52dB across two real-world datasets.

Timely detection and diagnosis of diseases are key elements of an efficient healthcare system. In recent years, artificial intelligence (AI) has played an increasingly important role in improving the accuracy and efficiency of disease diagnosis in clinical practice. However, most existing AI systems for disease diagnosis have focused on either classifying patients into broad disease categories or diagnosing a specific disease, leaving a gap in the development of a coherent AI system for both triage and diagnosis in a department of a general hospital. In this study, we fill this gap with SmartGyne, an advanced AI system that can achieve high-quality triage and diagnosis for a full spectrum of gynecological diseases. By extracting useful clinical evidence for diagnosis from a large amount of electronic medical records, SmartGyne establishes an effective framework to integrate real-world clinical evidence and knowledge into a coherent AI system that can effectively handle a full spectrum of complex diseases in a department of a general hospital. Validation experiments demonstrated that SmartGyne achieved an overall accuracy of 80.1 % in triage for gynecological diseases, and 99.4 % in diagnosis for a gynecological subspecialty. In comparison with human physicians, SmartGyne showed competitive triage and diagnostic performance, and improved consultation efficiency and accuracy for physicians with limited specialized experience. These results show that SmartGyne achieves high-quality triage and diagnosis, holding the potential to improve the efficiency of the healthcare system in China, as well as other countries lacking professional gynecologists.

With high temporal resolution, high dynamic range, and low latency, event cameras have made great progress in numerous low-level vision tasks. To help restore low-quality (LQ) video sequences, most existing event-based methods usually employ convolutional neural networks (CNNs) to extract sparse event features without considering the spatial sparse distribution or the temporal relation in neighboring events. It brings about insufficient use of spatial and temporal information from events. To address this problem, we propose a new spiking-convolutional network (SC-Net) architecture to facilitate event-driven video restoration. Specifically, to properly extract the rich temporal information contained in the event data, we utilize a spiking neural network (SNN) to suit the sparse characteristics of events and capture temporal correlation in neighboring regions; to make full use of spatial consistency between events and frames, we adopt CNNs to transform sparse events as an extra brightness prior to being aware of detailed textures in video sequences. In this way, both the temporal correlation in neighboring events and the mutual spatial information between the two types of features are fully explored and exploited to accurately restore detailed textures and sharp edges. The effectiveness of the proposed network is validated in three representative video restoration tasks: deblurring, super-resolution, and deraining. Extensive experiments on synthetic and real-world benchmarks have illuminated that our method performs better than existing competing methods.