论文标题
组织学区域鉴定的迭代迭代聚类
Seeded iterative clustering for histology region identification
论文作者
论文摘要
为了开发针对组织病理学的计算机视觉算法所必需的注释，但是高分辨率的密集注释通常很耗时。进行分割的深度学习模型是减轻过程的一种方法，但需要大量的培训数据，培训时间和计算能力。为了解决这些问题，我们提出了迭代聚类，以在整个幻灯片水平上密集地产生粗分割。该算法使用预先计算的表示作为聚类空间，而有限量的稀疏交互注释作为迭代分类的种子。我们获得了一种快速有效的方法，为整个幻灯片图像生成密集注释以及一个允许在转移学习背景下比较神经网络潜在表示的框架。
Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make. Deep learning models for segmentation are a way to alleviate the process, but require large amounts of training data, training times and computing power. To address these issues, we present seeded iterative clustering to produce a coarse segmentation densely and at the whole slide level. The algorithm uses precomputed representations as the clustering space and a limited amount of sparse interactive annotations as seeds to iteratively classify image patches. We obtain a fast and effective way of generating dense annotations for whole slide images and a framework that allows the comparison of neural network latent representations in the context of transfer learning.
