论文标题
组织病理学图像中的语义领域对抗
Inter-Semantic Domain Adversarial in Histopathological Images
论文作者
论文摘要
在计算机视觉中，数据转移已被证明是安全，强大的深度学习应用程序的主要障碍。在医疗应用中，组织病理学图像通常与数据转移有关，几乎无法使用。重要的是要了解使用所有可用数据在多大程度上可以在何种程度上对数据移动进行鲁棒化。在这里，我们首先表明域对抗方法如果错误地使用了域可能会非常有害。然后，我们使用域对抗方法将数据转移不变性从一个数据集传输到具有不同语义的另一个数据集，并表明域对抗方法在跨层次上具有比相似的性能相比，具有相似的性能。
In computer vision, data shift has proven to be a major barrier for safe and robust deep learning applications. In medical applications, histopathological images are often associated with data shift and they are hardly available. It is important to understand to what extent a model can be made robust against data shift using all available data. Here, we first show that domain adversarial methods can be very deleterious if they are wrongly used. We then use domain adversarial methods to transfer data shift invariance from one dataset to another dataset with different semantics and show that domain adversarial methods are efficient inter-semantically with similar performance than intra-semantical domain adversarial methods.
