Deep Learning Based Medical

Deep learning is revolutionizing medical image segmentation, aiming to automate the precise identification of anatomical structures and lesions within medical scans. Current research heavily focuses on improving segmentation accuracy and robustness using advanced architectures like UNet and Transformers, often incorporating techniques like active learning, data augmentation (including novel lesion-level approaches), and uncertainty quantification to address challenges such as limited data and model reliability. These advancements hold significant promise for improving diagnostic accuracy, streamlining clinical workflows, and accelerating medical research by enabling more efficient and objective analysis of medical images.