论文标题
使用无监督学习的数字病理的全身分类全图像幻灯片
Generalized Categorisation of Digital Pathology Whole Image Slides using Unsupervised Learning
论文作者
论文摘要
该项目旨在将大型病理图像分解为小图块，然后将这些瓷砖群集成不同的群体，而我们的分析表明，聚类肿瘤和非肿瘤细胞的某些方面的困难可能是多么困难，并且还表明，比较不同无人看管的方法的结果并不是一项琐碎的任务。该项目还提供了一个软件包，用于数字病理社区使用，该软件包使用开发的一些方法来执行无监督的无监督瓷砖分类，然后可以轻松地手动标记。 该项目使用了各种技术的混合物，从经典的聚类算法（例如K-均值和高斯混合模型）到更复杂的功能提取技术，例如深度自动编码器和多损失学习。在整个项目中，我们尝试使用一些措施，例如完整分数和群集图设定基准进行评估。 在我们的结果中，我们表明，卷积自动编码器由于其强大的内部表示能力而设法略微优于其余方法。此外，我们表明高斯混合模型的平均成果比K-均值更好，因为它在捕获不同的簇方面的灵活性。我们还显示了对不同类型的病理纹理进行分类的困难的巨大差异。
This project aims to break down large pathology images into small tiles and then cluster those tiles into distinct groups without the knowledge of true labels, our analysis shows how difficult certain aspects of clustering tumorous and non-tumorous cells can be and also shows that comparing the results of different unsupervised approaches is not a trivial task. The project also provides a software package to be used by the digital pathology community, that uses some of the approaches developed to perform unsupervised unsupervised tile classification, which could then be easily manually labelled. The project uses a mixture of techniques ranging from classical clustering algorithms such as K-Means and Gaussian Mixture Models to more complicated feature extraction techniques such as deep Autoencoders and Multi-loss learning. Throughout the project, we attempt to set a benchmark for evaluation using a few measures such as completeness scores and cluster plots. Throughout our results we show that Convolutional Autoencoders manages to slightly outperform the rest of the approaches due to its powerful internal representation learning abilities. Moreover, we show that Gaussian Mixture models produce better results than K-Means on average due to its flexibility in capturing different clusters. We also show the huge difference in the difficulties of classifying different types of pathology textures.
