Anatomy Aware

Anatomy-aware approaches in medical image analysis aim to improve the accuracy and generalizability of deep learning models by incorporating anatomical information. Current research focuses on integrating anatomical knowledge into various model architectures, including convolutional neural networks (CNNs), transformers, and graph neural networks, often using techniques like multi-modal prompting, contrastive learning, and auxiliary tasks to leverage anatomical priors. This improves performance in tasks such as lesion segmentation, disease detection, and report generation, ultimately leading to more accurate and reliable diagnostic tools and potentially personalized treatment planning.