Deep Learning for Robust and Generalizable Medical Image Segmentation

This thesis focuses on developing deep learning methods for medical image segmentation that are robust and can generalize across different clinical environments. The goal is to improve the reliability of segmentation models when applied to data from different hospitals, imaging devices, and acquisition settings, addressing one of the key challenges in real-world medical image analysis.