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。