论文标题
RGMIM：用于从X射线图像中学习有意义表示的区域引导的蒙版图像建模
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
论文作者
论文摘要
在这项研究中，我们提出了一种新的方法，称为区域引导的蒙版图像建模（RGMIM），用于从X射线图像中学习有意义的表示。我们的方法采用了一种新的掩蔽策略，该策略利用器官掩蔽信息来确定有效的区域，以学习更有意义的表示。我们对开放的肺X射线图像数据集以及掩盖比率超参数研究进行定量评估。当使用整个训练集时，RGMIM的表现优于其他可比方法，达到了0.962个肺部疾病检测准确性。具体而言，与其他方法相比，RGMIM显着提高了小数据量的性能，例如训练集的5％和10％。 RGMIM可以掩盖更有效的区域，从而促进歧视性代表的学习和随后的高准确性肺部疾病检测。 RGMIM的表现优于实验中其他最先进的自我监督学习方法，尤其是在使用有限的培训数据时。
In this study, we propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. Our method adopts a new masking strategy that utilizes organ mask information to identify valid regions for learning more meaningful representations. We conduct quantitative evaluations on an open lung X-ray image dataset as well as masking ratio hyperparameter studies. When using the entire training set, RGMIM outperformed other comparable methods, achieving a 0.962 lung disease detection accuracy. Specifically, RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set compared to other methods. RGMIM can mask more valid regions, facilitating the learning of discriminative representations and the subsequent high-accuracy lung disease detection. RGMIM outperforms other state-of-the-art self-supervised learning methods in experiments, particularly when limited training data is used.
