Northeastern University（东北大学）

Progressive Masked Refinement Self-supervised Learning for Low-Dose CT Denoising

渐进式 $\mathcal{J}$-不变自监督学习用于低剂量CT去噪

Yichao Liu, Zongru Shao, Rui Wen, Yueyang Teng, Junwen Guo

机构 * organization= IWR, Heidelberg University , city= Heidelberg , postcode= 69120 , state= Baden Württemberg , country= Germany ； organization= Silicon Austria Labs , city= Linz , postcode= 4040 , state= Upper Austria , country= Austria ； organization= Institute of Science Tokyo , addressline= , city= Tokyo , country= Japan ； organization= College of Medicine ； Biological Information Engineering, Northeastern University , city= Shenyang , postcode= 110169 , state= Liaoning , country= China ； organization= Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education , city= Shenyang , postcode= 110169 , state= Liaoning , country= China ； organization= Department of Epidemiology \& Global Health, Umeå University , addressline= , city= Umeå , postcode= 90187 , country= Sweden 纠错

AI总结 提出渐进式 $\mathcal{J}$-不变学习，通过逐步盲点去噪机制和噪声注入正则化，提升低剂量CT去噪性能，在Mayo数据集上优于现有自监督方法并接近监督方法。

Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles

动态感知：面向自动驾驶轨迹预测的自适应多模式异常检测

Tongfei Guo, Lili Su

机构 * Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系） 纠错

AI总结 本文提出一种自适应多模式异常检测框架，通过建模误差模式提升轨迹预测的鲁棒性，在复杂驾驶环境中实现更高效的异常检测。

Comments Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)