Tumor Segmentation Task

Tumor segmentation, the automated identification of tumor boundaries in medical images, aims to improve diagnostic accuracy and treatment planning. Current research heavily utilizes deep learning, focusing on advanced architectures like Transformers and U-Nets, often incorporating multi-modal data (e.g., MRI sequences) and addressing challenges like data scarcity through techniques such as federated learning and data synthesis. These advancements are crucial for improving the efficiency and accuracy of cancer diagnosis and treatment, particularly in scenarios with limited annotated data or privacy concerns.