论文标题
漫游：在医学成像中进行半监督学习的随机层混合
ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
论文作者
论文摘要
医疗图像分割是机器学习方法所面临的主要挑战之一。然而，深度学习方法在很大程度上取决于大量注释的数据，这些数据既耗时又昂贵。但是，半监督学习方法通过利用大量未标记的数据以及培训过程中的少量标记数据来解决此问题。最近，Mixup正常制剂已成功地引入了半监督的学习方法，显示出卓越的性能。混音通过在输入空间上的数据线性插值来增强模型的新数据点。我们认为此选项是有限的。取而代之的是，我们提出了漫游，这是一个随机层混合，它鼓励网络对随机选择空间的插值数据点的信心降低。 ROAM生成了更多从未见过的数据点，因此避免了过度拟合并增强了概括能力。我们进行了广泛的实验，以验证我们在全脑图像分段的三个公开可用数据集上的方法。漫游实现最新的（SOTA）会导致全面监督（89.5％）和半监督（87.0％）的设置，分别相对改善高达2.40％和16.50％，分别为整个脑部细分。
Medical image segmentation is one of the major challenges addressed by machine learning methods. Yet, deep learning methods profoundly depend on a large amount of annotated data, which is time-consuming and costly. Though, semi-supervised learning methods approach this problem by leveraging an abundant amount of unlabeled data along with a small amount of labeled data in the training process. Recently, MixUp regularizer has been successfully introduced to semi-supervised learning methods showing superior performance. MixUp augments the model with new data points through linear interpolation of the data at the input space. We argue that this option is limited. Instead, we propose ROAM, a RandOm lAyer Mixup, which encourages the network to be less confident for interpolated data points at randomly selected space. ROAM generates more data points that have never seen before, and hence it avoids over-fitting and enhances the generalization ability. We conduct extensive experiments to validate our method on three publicly available datasets on whole-brain image segmentation. ROAM achieves state-of-the-art (SOTA) results in fully supervised (89.5%) and semi-supervised (87.0%) settings with a relative improvement of up to 2.40% and 16.50%, respectively for the whole-brain segmentation.
