Concetti Chiave
This research paper introduces AIGCMatch, a novel semi-supervised learning framework that leverages attention-guided perturbations at both the image and feature levels to improve the accuracy and efficiency of medical image segmentation models, particularly in scenarios with limited labeled data.
Cheng, Y., Shao, C., Ma, J., & Li, G. (2024). Attention-Guided Perturbation for Consistency Regularization in Semi-Supervised Medical Image Segmentation. arXiv preprint arXiv:2410.12419.
This paper addresses the challenge of limited labeled data in medical image segmentation by proposing a novel semi-supervised learning framework called AIGCMatch (Attention-Guided Consistency regularization Match). The study aims to enhance the performance of medical image segmentation models by leveraging attention-guided perturbations for consistency regularization.