An efficient and precise diagnosis of retinal diseases is a fundamental goal for auxiliary diagnostic systems in ophthalmology. Inspired by the importance of scattered subtle lesions in manual retinal disease diagnosis, recent research has achieved state-of-the-art performance by mining information related to subtle lesions, including their texture and shape. However, the spatial distribution patterns of subtle lesion areas, which are also crucial in manual diagnosis, have been overlooked in existing research. Neglecting these spatial distribution patterns (e.g., the ring distribution of microaneurysms in diabetic macular edema) may negatively impact the diagnostic process. In this paper, we introduce the Saliency-Image-Graph (SIGraph) network to capture the spatial distribution patterns of lesion areas. We first employ saliency-based perception to identify latent lesion pixels. Subsequently, we propose a novel image-graph block to efficiently capture the global distribution of abundant lesion pixels with minimal information loss. By leveraging additional distribution patterns, SIGraph achieves state-of-the-art performance with at least a 1.5% performance gain across three datasets. Furthermore, ablation studies demonstrate that our image-graph block can be integrated into other visual backbones and effectively boost performance.

Orthodontic path planning is crucial for precise treatment, yet AI -generated trajectories remain inferior to expert designs due to difficulties in perceiving large dental point clouds and modeling multi-step tooth displacements. In this paper, we introduce Efficient ToothAR, a cascade autoregressive framework that decomposes orthodontic planning into tooth perception and transition prediction stages for improved efficiency and accuracy. In the perception stage, we adapt the efficient sequence model Mamba with a tooth-by-tooth scan and hierarchical architecture to encode dental point clouds. In the transition stage, a bidirectional transformer predicts sequential tooth movements autore-gressively from both initial and target configurations, effectively reducing accumulated transition errors. Trained on 5,000 clinical alignment trajectories, Efficient ToothAR achieves state-of-the-art performance across multiple metrics (e.g., displacement error, collision rate) while maintaining high computational efficiency.

The six-degree-of-freedom (6-DOF) robotic arm has gained widespread application in human-coexisting environments. While previous research has predominantly focused on functional motion generation, the critical aspect of expressive motion in human-robot interaction remains largely unexplored. This paper presents a novel real-time motion generation planner that enhances interactivity by creating expressive robotic motions between arbitrary start and end states within predefined time constraints. Our approach involves three key contributions: first, we develop a mapping algorithm to construct an expressive motion dataset derived from human dance movements; second, we train motion generation models in both Cartesian and joint spaces using this dataset; third, we introduce an optimization algorithm that guarantees smooth, collision-free motion while maintaining the intended expressive style. Experimental results demonstrate the effectiveness of our method, which can generate expressive and generalized motions in under 0.5 seconds while satisfying all specified constraints.

Accurate fall detection systems are vital to address the global health concern of elderly falls, which often lead to severe injuries, hospitalizations, and fatalities. Since falls can happen at any time in any location, it is imperative to have a comprehensive system that boasts high applicability across a broad range of scenarios, operating seamlessly 24/7. However, within a range of fall detection systems, most of the existing work is built upon mono-modal sensors, which are inevitably inherited and constrained by mono-modal shortages. To overcome the constraints of mono-modal systems, we introduce VRFfall, a novel multi-modal fall detection system that seamlessly fuses mmWave radar and camera technologies. As a system with high generalization capabilities, VRFfall supports both multi-modal and mono-modal inputs with its independent feature extraction pipeline for each modality. Utilizing a cross-modal knowledge transfer design, VRFfall enhances performance with mono-modal input by leveraging fused knowledge from the other modality. Moreover, to ensure optimal fusion decisions under modal discrepancies, VRFfall incorporates an adaptive Modal Quality Assessment Module (MQAM) that dynamically evaluates and fuses features from both modalities. Extensive evaluations using a dataset collected from 20 volunteers across two environments and three conditions have been conducted on VRFfall. The results demonstrate its high performance and excellent generalization across diverse environments and conditions, promising a 24/7 continuous fall detection system.