Risk Segmentation

Risk segmentation research focuses on identifying and categorizing risks, particularly within AI systems and medical image analysis. Current efforts involve developing comprehensive risk taxonomies based on regulatory frameworks and establishing standardized benchmarks for evaluating AI safety, alongside algorithmic advancements in model architectures like U-Net and GANs to improve the accuracy and robustness of automated segmentation in medical imaging. These advancements are crucial for improving AI safety and reliability, as well as enhancing the efficiency and accuracy of medical procedures like radiation therapy planning.