Pathology Segmentation

Pathology segmentation aims to automatically identify and delineate diseased tissue regions within medical images, improving diagnostic accuracy and efficiency. Current research heavily utilizes deep learning, focusing on U-Net architectures and their variations, often incorporating multi-modal image inputs (e.g., combining CT and pathology images) or leveraging unsupervised anomaly detection methods trained on healthy data. These advancements are crucial for improving the speed and accuracy of disease diagnosis, particularly in areas like cancer subtyping and brain lesion detection, ultimately leading to better patient care and treatment planning.