Kidney Parsing

Kidney parsing, the automated segmentation and identification of kidney structures (including tumors, arteries, and veins) in medical images, aims to improve the accuracy and efficiency of renal cancer diagnosis and treatment planning. Current research heavily utilizes deep learning architectures, such as U-Net and YOLO variants, often incorporating techniques like knowledge distillation to enhance model performance and address challenges posed by variations in tumor size and ambiguous boundaries. These advancements hold significant promise for improving the precision of computer-aided diagnosis and potentially streamlining surgical procedures, ultimately leading to better patient outcomes.