Quantification of lymphoid aggregates including tertiary lymphoid structures
with germinal centers in histology images of cancer is a promising approach for
developing prognostic and predictive tissue biomarkers. In this article, we
provide recommendations for identifying lymphoid aggregates in tissue sections
from routine pathology workflows such as hematoxylin and eosin staining. To
overcome the intrinsic variability associated with manual image analysis (such
as subjective decision making, attention span), we recently developed a deep
learning-based algorithm called HookNet-TLS to detect lymphoid aggregates and
germinal centers in various tissues. Here, we additionally provide a guideline
for using manually annotated images for training and implementing HookNet-TLS
for automated and objective quantification of lymphoid aggregates in various
cancer types.

使用深度学习算法 HookNet-TLS 对组织样本中的淋巴聚集及生发中心进行自动化与客观化定量，提供了使用人工标记图像训练和实施该算法的指南。