polyp segmentation, a contentious issue in medical imaging, has seen numerous
proposed methods aimed at improving the quality of segmented masks. Currently,
state-of-the-art techniques yield impressive results. However, the sheer size
of these models poses challenges for practical indu
本文提出了一种基于深度学习的半监督知识蒸馏方法,通过标注和未标注的数据进行数据训练,建立了一个包含教师和学生网络的 Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining (MMT-PSM)。实验表明,该方法相对于只学习标注数据的监督方法和最先进的半监督方法,显著提高了性能。