This study evaluates the generalisation capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data, on Rheumatoid Arthritis subtyping and Sjogren's Disease detection tasks. Using a simple Attention-Based Multiple Instance Learning classifier, we assess the transferability of learned representations from cancer H&E images to autoimmune IHC images. Contrary to expectations, histopathology-pretrained models did not significantly outperform ImageNet-pretrained models. Furthermore, there was evidence of both autoimmune feature misinterpretation and biased feature importance. Our findings highlight the challenges in transferring knowledge from cancer to autoimmune histopathology and emphasise the need for careful evaluation of AI models across diverse histopathological tasks. The code to run this benchmark is available at https://github.com/AmayaGS/ImmunoHistoBench.

本研究探讨了先进的组织病理学基础模型在多染色自身免疫免疫组化数据集上的泛化能力。研究表明，尽管在肿瘤病理学上表现良好，这些模型在处理自身免疫性疾病时的有效性和重要性却存在问题，揭示了从癌症到自身免疫组织病理学知识转移的挑战。该研究强调了在不同组织病理学任务中仔细评估AI模型的重要性。

超越H