Lesion Segmentation

Lesion segmentation in medical imaging aims to automatically identify and delineate regions of pathology within medical scans, improving diagnostic accuracy and streamlining clinical workflows. Current research emphasizes the development of robust and generalizable deep learning models, frequently employing architectures like U-Net, transformers, and YOLO variants, often incorporating techniques such as multi-task learning, attention mechanisms, and data augmentation to address challenges posed by data heterogeneity and variability in lesion characteristics. These advancements hold significant promise for improving the speed and accuracy of disease diagnosis, treatment planning, and monitoring of therapeutic response across various medical specialties.