Segmentation Assisted Diagnosis

Segmentation-assisted diagnosis leverages image segmentation techniques to improve the accuracy and efficiency of medical diagnoses. Current research focuses on developing robust segmentation models, often employing deep learning architectures like U-Net and Transformers, that can handle variations in imaging protocols and inter-observer variability in annotations. This is achieved through techniques such as co-registration, attention mechanisms, and novel loss functions that prioritize diagnostic accuracy. Improved segmentation accuracy translates to more reliable diagnostic tools, ultimately leading to better patient care and more efficient workflows in healthcare.