论文标题
半监督病变细分的剪切一致性学习
Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation
论文作者
论文摘要
半监督学习的潜力有可能提高培训数据渴望的深神经网络的数据效率，这对于稀缺标记数据的医学图像分析任务尤为重要。在这项工作中，我们基于剪切的增强和一致性正则化的思想，提出了一种简单的半监督学习方法，用于病变细分任务。通过利用标记数据中可用的掩码信息，我们可以从未标记的图像中合成部分标记的样品，以便可以应用通常的监督学习目标（例如，二进制跨熵）。此外，我们引入了一个背景一致性项，以使合成图像未标记的背景区域的训练正规化。我们从经验上验证了所提出的方法对两个公共病变细分数据集的有效性，包括眼睛眼底照片数据集和脑CT扫描数据集。实验结果表明，我们的方法在不引入复杂的网络组件的情况下，与其他基于基于一致性的方法相比，达到了一致和卓越的性能。
Semi-supervised learning has the potential to improve the data-efficiency of training data-hungry deep neural networks, which is especially important for medical image analysis tasks where labeled data is scarce. In this work, we present a simple semi-supervised learning method for lesion segmentation tasks based on the ideas of cut-paste augmentation and consistency regularization. By exploiting the mask information available in the labeled data, we synthesize partially labeled samples from the unlabeled images so that the usual supervised learning objective (e.g., binary cross entropy) can be applied. Additionally, we introduce a background consistency term to regularize the training on the unlabeled background regions of the synthetic images. We empirically verify the effectiveness of the proposed method on two public lesion segmentation datasets, including an eye fundus photograph dataset and a brain CT scan dataset. The experiment results indicate that our method achieves consistent and superior performance over other self-training and consistency-based methods without introducing sophisticated network components.
