Risk Localization

Risk localization focuses on accurately identifying and defining the spatial extent of risky elements within various contexts, from autonomous driving to medical imaging. Current research emphasizes developing robust and efficient models, including multimodal large language models and 3D convolutional neural networks, to achieve this, often incorporating self-supervised learning techniques to reduce reliance on labeled data. These advancements are crucial for improving the safety and reliability of autonomous systems and enhancing the precision of medical procedures like radiation therapy planning, ultimately leading to better decision-making in high-stakes applications.