Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels

Medical image segmentation plays a pivotal role in medical imaging, significantly contributing to disease diagnosis and surgical planning. Traditional segmentation methods predominantly rely on supervised deep learning, where the accuracy of manually delineated labels is crucial for model performance. However, these labels often contain noise, such as missing annotations and imprecise boundaries, which can adversely affect the network's ability to accurately model target characteristics.