Fig. 3

The schematic diagram of contrastive learning. (a) represents a schematic diagram of applying contrastive learning loss. Unlabeled samples are simultaneously fed into both the teacher and student networks. The features generated by the student model’s encoder are projected through a mapping layer, and the segmentation results from the teacher model act as pseudo-labels, assigning class information to each voxel of the student model’s encoder output. The contrastive learning loss is then calculated. (b) demonstrates the updated rules for storing features in the memory bank. The memory bank functions as a fixed-length queue that operates on a first-in, first-out (FIFO) basis. When labeled data is input, the teacher model generates feature representations and segmentation results. High-quality feature vectors are selected based on these segmentation results and corresponding labels and are pushed into the memory bank, while the oldest feature vectors are popped out