Lymph Node

Lymph node analysis is crucial for cancer diagnosis, staging, and treatment planning, necessitating accurate and efficient detection and segmentation. Current research heavily focuses on developing and improving automated methods using deep learning architectures like U-Net, transformers (DETR, Swin-Transformer), and diffusion models, often incorporating anatomical priors or weakly supervised learning techniques to address challenges posed by limited annotated data and the variability in lymph node appearance. These advancements aim to improve the speed and accuracy of lymph node assessment, ultimately leading to better patient care and more efficient clinical workflows. The development of large, publicly available datasets is also a key focus, facilitating the development and validation of robust algorithms.