Semi Supervised Medical Image Segmentation

Semi-supervised medical image segmentation is a crucial branch of computer vision that aims to achieve precise segmentation of medical images using a limited amount of labeled data and a large amount of unlabeled data. This technique combines methods from supervised and unsupervised learning to enhance the model's generalization ability and segmentation accuracy, reducing the reliance on extensive labeled data. Its primary goal is to enable the automatic identification and segmentation of specific structures or regions of interest in medical images, thereby assisting doctors in diagnosis, treatment planning, and disease assessment. Semi-supervised medical image segmentation holds significant value in clinical applications, as it can substantially improve medical efficiency and accuracy.