Tumor Detection

Tumor detection research focuses on developing accurate and efficient methods for identifying cancerous growths in medical images, aiming to improve early diagnosis and treatment outcomes. Current efforts leverage deep learning models, including convolutional neural networks (CNNs) like U-Net and transformers (e.g., Swin Transformers), often incorporating techniques like multi-task learning, semi-supervised learning, and data augmentation to address challenges such as limited annotated data and class imbalance. These advancements hold significant promise for improving diagnostic accuracy, accelerating workflows, and ultimately enhancing patient care.