论文标题
组织病理学图像的分割和分类的数据有效的深度学习框架
A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images
论文作者
论文摘要
通常用于诊断和研究目的的组织病理学图像中炎症细胞结构的当前研究排除了许多有关活检幻灯片的信息。在自身免疫性疾病中，关于哪种细胞类型在组织水平上参与炎症以及它们如何相互作用的主要研究问题。尽管这些问题可以通过传统方法部分回答，但人工智能方法进行分割和分类提供了一种更有效的方法来了解自身免疫性疾病中炎症的结构，并对新见解保持了巨大的希望。在本文中，我们从经验上开发了使用人类组织的皮肌炎活检来检测和鉴定炎症细胞的深度学习方法。我们的方法将分类性能提高了26％，细分性能提高了5％。我们还提出了一种新型的后处理自动编码器体系结构，可将细分性能额外提高3％。
The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding research questions remain regarding which cell types participate in inflammation at the tissue level, and how they interact with each other. While these questions can be partially answered using traditional methods, artificial intelligence approaches for segmentation and classification provide a much more efficient method to understand the architecture of inflammation in autoimmune disease, holding great promise for novel insights. In this paper, we empirically develop deep learning approaches that use dermatomyositis biopsies of human tissue to detect and identify inflammatory cells. Our approach improves classification performance by 26% and segmentation performance by 5%. We also propose a novel post-processing autoencoder architecture that improves segmentation performance by an additional 3%.
