标签丢弃：利用多个数据集与领域偏移和部分标注改进深度学习超声分割
Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling
AI总结：
本文提出标签丢弃方法，通过多数据集训练提升超声分割鲁棒性，显著提高二分类Dice分数。
摘要：
超声是评估心脏功能时首先使用的成像模态。从超声中测量功能生物标记物依赖于心脏结构的分割，深度学习模型已被提出自动执行分割。然而，为了将这些工具推广到临床应用，分割模型必须对各种图像具有鲁棒性（例如来自不同扫描仪、不同技术水平的操作员等）。为实现这种鲁棒性，模型必须在多个多样化数据集上进行训练。在使用多个多样化数据集训练时面临的主要挑战是标签存在的变化，即综合数据通常部分标注。已经提出了交叉熵损失函数的适应性来处理部分标注数据。在本文中，我们显示，使用此类损失函数和多个多样化数据集进行训练可能导致一种捷径学习，即模型将标签存在与领域特征联系起来，导致性能下降。为了解决这个问题，我们提出了一种新的标签丢弃方案，以打破领域特征与标签存在或不存在之间的联系。我们证明，当使用多个多样化部分标注数据集进行训练时，标签丢弃可将超声分割Dice分数提高62%和25%。
英文摘要：
Echocardiography (echo) is the first imaging modality used when assessing cardiac function. The measurement of functional biomarkers from echo relies upon the segmentation of cardiac structures and deep learning models have been proposed to automate the segmentation process. However, in order to translate these tools to widespread clinical use it is important that the segmentation models are robust to a wide variety of images (e.g. acquired from different scanners, by operators with different levels of expertise etc.). To achieve this level of robustness it is necessary that the models are trained with multiple diverse datasets. A significant challenge faced when training with multiple diverse datasets is the variation in label presence, i.e. the combined data are often partially-labelled. Adaptations of the cross entropy loss function have been proposed to deal with partially labelled data. In this paper we show that training naively with such a loss function and multiple diverse datasets can lead to a form of shortcut learning, where the model associates label presence with domain characteristics, leading to a drop in performance. To address this problem, we propose a novel label dropout scheme to break the link between domain characteristics and the presence or absence of labels. We demonstrate that label dropout improves echo segmentation Dice score by 62% and 25% on two cardiac structures when training using multiple diverse partially labelled datasets.
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