Fig. 2
From: A dynamic few-shot learning framework for medical image stream mining based on self-training
Overview of the proposed self-training method for medical semantic segmentation. It mainly contains two parts. (1) Proxy-based pseudo-label generator (top half): we input the unlabeled frames into a momentum model, project the features into embedding space by \(e(\cdot )\), and then generate pseudo-labels \(\hat{Y}_{U}\) according to its distance to each proxy. (2) Augmentation-Recombination Module (bottom half): we split the unlabeled sequences into augmented images by different augmentation methods, such as cropped, rotate, and color jittering, and then input them to the model. Through the classifier \(c(\cdot )\) and recombination, we obtain the prediction. Finally, we calculate the cross-entropy loss on \(\hat{Y}_{t}\) and \(p_t\). Note that the proxy