Process of labeling or pseudo-labeling for unlabeled samples. Following the training phase in the ITS, we select K hard samples from the pool of (N_−test − M) unlabeled data and label them with expert annotations. Subsequently, after the training phase in the ALS, we choose V over-threshold samples from (N_−test − M − K) unlabeled data to assign pseudo-labels. Moving forward, after the training process in SSLS in the r-th iteration, we again select V samples from the pool of (N_−test − M − K) unlabeled data that exceed the predetermined threshold and assign pseudo labels. In the (r + 1)-th iteration, we retrain the semi-supervised training model from scratch, refining the performance of our models in subsequent iterations.