Robust Medical Image Segmentation

Robust medical image segmentation aims to develop accurate and reliable algorithms for automatically delineating anatomical structures in medical images, even when faced with variations in image quality, acquisition protocols, or patient populations. Current research emphasizes hybrid architectures combining convolutional neural networks (CNNs) with transformers to leverage both local and global image features, and explores techniques like knowledge distillation, curriculum learning, and implicit neural representations to improve generalization and robustness. These advancements are crucial for improving the accuracy and reliability of computer-aided diagnosis and treatment planning, ultimately leading to better patient care.