Pathologists routinely use immunohistochemical (IHC)-stained tissue slides
against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to
improve their accuracy in diagnosing melanomas. The use of diagnostic Deep
Learning (DL)-based support systems for automated examination of tissue
morphology and cellular composition has been well studied in standard
H&E-stained tissue slides. In contrast, there are few studies that analyze IHC
slides using DL. Therefore, we investigated the separate and joint performance
of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA
classifier achieved an area under receiver operating characteristics curve
(AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the
H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined
classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD
datasets. DL MelanA-based assistance systems show the same performance as the
benchmark H&E classification and may be improved by multi stain classification
to assist pathologists in their clinical routine.

通过研究利用 MelanA 和 H&E 染色组织切片的深度学习支持系统，我们发现 MelanA 分类器与基准 H&E 分类器表现相似，可以通过多染色分类进一步提高性能，为病理学家的临床工作提供帮助。

评估基于深度学习的黑色素瘤分类研究：免疫组织化学和常规组织学的三中心研究

Evaluating Deep Learning-based Melanoma Classification using  Immunohistochemistry and Routine Histology: A Three Center Study

For invasive breast cancer, immunohistochemical (IHC) techniques are often
used to detect the expression level of human epidermal growth factor receptor-2
(HER2) in breast tissue to formulate a precise treatment plan. From the
perspective of saving manpower, material and time costs, directly generating
IHC-stained images from hematoxylin and eosin (H&E) stained images is a
valuable research direction. Therefore, we held the breast cancer
immunohistochemical image generation challenge, aiming to explore novel ideas
of deep learning technology in pathological image generation and promote
research in this field. The challenge provided registered H&E and IHC-stained
image pairs, and participants were required to use these images to train a
model that can directly generate IHC-stained images from corresponding
H&E-stained images. We selected and reviewed the five highest-ranking methods
based on their PSNR and SSIM metrics, while also providing overviews of the
corresponding pipelines and implementations. In this paper, we further analyze
the current limitations in the field of breast cancer immunohistochemical image
generation and forecast the future development of this field. We hope that the
released dataset and the challenge will inspire more scholars to jointly study
higher-quality IHC-stained image generation.

本文介绍了乳腺癌免疫组化图像生成挑战赛，旨在探索深度学习技术在病理图像生成领域的新思路并促进研究。我们进一步分析了该领域的当前限制并预测了未来的发展。发放的数据集和挑战将希望能激发更多学者共同研究更高质量的免疫组化染色图像的生成。