Robust Segmentation

Robust segmentation aims to create image segmentation models that are resilient to noise, variations in data acquisition, and adversarial attacks, ultimately improving the reliability and accuracy of automated image analysis. Current research focuses on enhancing model robustness through techniques like adversarial training, data augmentation informed by sensitivity analysis, and the incorporation of architectures such as U-Nets, transformers, and feature pyramid networks, often combined with novel loss functions or uncertainty quantification methods. These advancements are crucial for reliable applications across diverse fields, including medical imaging diagnosis, autonomous driving, and remote sensing, where accurate and dependable segmentation is paramount for effective decision-making.