Lesion Localization

Lesion localization in medical imaging aims to automatically identify and delineate regions of disease within scans, improving diagnostic accuracy and efficiency. Current research heavily utilizes deep learning, employing architectures like UNets, Swin Transformers, and convolutional neural networks, often enhanced with attention mechanisms and multi-scale feature extraction to handle variations in lesion size, shape, and appearance across different imaging modalities (e.g., PET/CT, MRI, ultrasound). These advancements are crucial for improving diagnostic accuracy, aiding in treatment planning (e.g., radiotherapy), and enabling quantitative analysis of lesion growth and response to therapy. The development of robust and generalizable models, particularly those addressing data scarcity and inter-observer variability, remains a key focus.