GeoCFNet: Geometry-Aware Confidence Field Network for Robot-Assisted Endoscopic Submucosal Dissection

Advanced surgical robotics has made robot-assisted endoscopic submucosal dissection (ESD) a promising approach for the en-bloc resection of large lesions, with the potential to reduce recurrence and improve long-term outcomes. However, the technical complexity and risk of complications in ESD demand stable and precise visual guidance to maintain an accurate dissection corridor and a safe tissue margin. Dense confidence fields provide an effective representation for this purpose by describing both the preferred dissection region and its spatial transition to surrounding tissue. However, reliable confidence field estimation remains challenging in dynamic endoscopic scenes due to smoke, specular highlights, tissue deformation, weak texture, and the thin geometric structure of the target region. To address these challenges, we formulate dissection guidance as a geometry-aware confidence field estimation problem and propose GeoCFNet, a geometry-aware confidence field network built on a pretrained DINOv3 backbone. GeoCFNet integrates a Token-Differentiated Fusion module to aggregate class-token context with dense patch representations, a SegFormer decoder for confidence regression, and Geometry-Aware Spatial Regularization (GASR) to preserve spatial coherence and local geometric transitions. Experimental results show that GeoCFNet achieves RMSE 0.0480, PSNR 27.1995, SSIM 0.3397, and CC 0.2466, indicating accurate and geometrically stable confidence field estimation for robot-assisted ESD guidance.

SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition

Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors is a cornerstone of mobile health, smart environments, and human-computer interaction. However, current deep learning-based HAR models often struggle with heavy reliance on labeled data, position-specific ambiguity, and a lack of transparent reasoning. Inspired by the advanced agents framework, which emulates a collaborative agent using Large Language Models (LLMs), we propose SensingAgents, a novel multi-agent system for robust IMU activity recognition. SensingAgents organizes LLM-powered agents into specialized roles: a group of Analyst Agents for position-specific sensor analysis (arm, wrist, belt, pocket), a pair of Advocate Agents that resolves sensor conflicts through dynamic and static dialectical debates, and a Decision Agent that ensures reliability under sensor drift or failure. Evaluation on the Shoaib dataset demonstrates that SensingAgents significantly outperforms state-of-the-art single-agent and multi-agent LLM models, achieving an accuracy of 79.5% in a zero setting--29% higher than existing agent models and 9.4% higher than deep learning baselines--particularly in complex scenarios where multi-sensor data is conflicting or noisy. Our work highlights the potential of multi-agent collaborative reasoning for advancing the robustness and interpretability of ubiquitous sensing systems.

Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data

Accurate depth perception is crucial for patient outcomes in endoscopic surgery, yet it is compromised by image distortions common in surgical settings. To tackle this issue, our study presents a benchmark for assessing the robustness of endoscopic depth estimation models. We have compiled a comprehensive dataset that reflects real-world conditions, incorporating a range of synthetically induced corruptions at varying severity levels. To further this effort, we introduce the Depth Estimation Robustness Score (DERS), a novel metric that combines measures of error, accuracy, and robustness to meet the multifaceted requirements of surgical applications. This metric acts as a foundational element for evaluating performance, establishing a new paradigm for the comparative analysis of depth estimation technologies. Additionally, we set forth a benchmark focused on robustness for the evaluation of depth estimation in endoscopic surgery, with the aim of driving progress in model refinement. A thorough analysis of two monocular depth estimation models using our framework reveals crucial information about their reliability under adverse conditions. Our results emphasize the essential need for algorithms that can tolerate data corruption, thereby advancing discussions on improving model robustness. The impact of this research transcends theoretical frameworks, providing concrete gains in surgical precision and patient safety. This study establishes a benchmark for the robustness of depth estimation and serves as a foundation for developing more resilient surgical support technologies. Code is available at https://github.com/lofrienger/EndoDepthBenchmark.