论文标题
监督和源域对表示学习的影响：组织病理学案例研究
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
论文作者
论文摘要
由于许多算法取决于数据的适当表示，因此学习独特的特征被认为是至关重要的任务。尽管使用深层神经网络的监督技术提高了表示学习的性能，但需要大量标记的数据限制了此类方法的应用。例如，由于较大的图像尺寸，病理领域感兴趣的区域的高质量描述是一项繁琐且耗时的任务。在这项工作中，我们探讨了代表学习领域中深层神经网络和三胞胎损失的性能。我们调查了病理全图像的相似性和相似性的概念，并比较了从无监督和半监督者到我们实验中监督学习的不同设置。此外，测试了不同的方法，在两个公开可用的病理图像数据集上应用了很少的学习。当将学习的表示形式应用于两个不同的病理数据集时，我们实现了很高的准确性和概括。
As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large set of labeled data limits the application of such methods. As an example, high-quality delineations of regions of interest in the field of pathology is a tedious and time-consuming task due to the large image dimensions. In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning. We investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning in our experiments. Additionally, different approaches were tested, applying few-shot learning on two publicly available pathology image datasets. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets.
