Hematoxylin and Eosin (H&E) staining is a widely used sample preparation
procedure for enhancing the saturation of tissue sections and the contrast
between nuclei and cytoplasm in histology images for medical diagnostics.
However, various factors, such as the differences in the reagents used, result
in high variability in the colors of the stains actually recorded. This
variability poses a challenge in achieving generalization for machine-learning
based computer-aided diagnostic tools. To desensitize the learned models to
stain variations, we propose the Generative Stain Augmentation Network (G-SAN)
-- a GAN-based framework that augments a collection of cell images with
simulated yet realistic stain variations. At its core, G-SAN uses a novel and
highly computationally efficient Laplacian Pyramid (LP) based generator
architecture, that is capable of disentangling stain from cell morphology.
Through the task of patch classification and nucleus segmentation, we show that
using G-SAN-augmented training data provides on average 15.7% improvement in F1
score and 7.3% improvement in panoptic quality, respectively. Our code is
available at this https URL

提出使用生成对抗网络 (GAN) 的 Generative Stain Augmentation Network (G-SAN) 来消除组织切片染色不同的变异性对机器学习诊断工具的影响，并通过补充染色变异性数据，使得机器学习模型更加健壮。通过组织学图像的核分类和分割任务的结果，证明使用 G-SAN 的增强数据平均提高了 F1 得分 15.7％和齐柏林物质的质量 7.3％。