Learning Beyond Labels: Understanding the Role of Imperfect Supervision in Medical Image Segmentation
Medical image segmentation often relies either on traditional image processing methods, which can be limited in their ability to handle complex image characteristics, or on deep learning approaches, which require large amounts of high-quality annotated data. Hybrid methods that combine these two paradigms offer a potential way to reduce annotation requirements while maintaining strong segmentation
