Anatomical Token

Anatomical tokenization in medical image analysis focuses on representing anatomical structures within medical images (e.g., X-rays, MRIs) as discrete, informative units for downstream tasks like segmentation, pathology detection, and report generation. Current research emphasizes the use of deep learning models, including convolutional neural networks (CNNs), U-Nets, and graph convolutional networks (GCNs), often incorporating techniques like weakly supervised learning and pseudo-labeling to address data scarcity and annotation challenges. This work aims to improve the accuracy and efficiency of automated image analysis, ultimately assisting radiologists and clinicians in diagnosis, treatment planning, and patient care by providing more precise and reliable information extracted from medical images.