H&E-to-IHC stain translation techniques offer a promising solution for
precise cancer diagnosis, especially in low-resource regions where there is a
shortage of health professionals and limited access to expensive equipment.
Considering the pixel-level misalignment of H&E-IHC image pairs, current
research explores the pathological consistency between patches from the same
positions of the image pair. However, most of them overemphasize the
correspondence between domains or patches, overlooking the side information
provided by the non-corresponding objects. In this paper, we propose a
Mix-Domain Contrastive Learning (MDCL) method to leverage the supervision
information in unpaired H&E-to-IHC stain translation. Specifically, the
proposed MDCL method aggregates the inter-domain and intra-domain pathology
information by estimating the correlation between the anchor patch and all the
patches from the matching images, encouraging the network to learn additional
contrastive knowledge from mixed domains. With the mix-domain pathology
information aggregation, MDCL enhances the pathological consistency between the
corresponding patches and the component discrepancy of the patches from the
different positions of the generated IHC image. Extensive experiments on two
H&E-to-IHC stain translation datasets, namely MIST and BCI, demonstrate that
the proposed method achieves state-of-the-art performance across multiple
metrics.

通过提出混合域对比学习（MDCL）方法，在未配对的 H&E 到 IHC 染色转换中利用不同领域的病理信息聚合，提高相应补丁之间的病理一致性和生成的 IHC 图像不同位置的补丁的不一致性组件，取得了多个度量标准下的最先进性能。

跨领域对比学习用于无配对的 H&E 到 IHC 染色转换

Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain  Translation

Question answering (QA) models have shown compelling results in the task of
Machine Reading Comprehension (MRC). Recently these systems have proved to
perform better than humans on held-out test sets of datasets e.g. SQuAD, but
their robustness is not guaranteed. The QA model's brittleness is exposed when
evaluated on adversarial generated examples by a performance drop. In this
study, we explore the robustness of MRC models to entity renaming, with
entities from low-resource regions such as Africa. We propose EntSwap, a method
for test-time perturbations, to create a test set whose entities have been
renamed. In particular, we rename entities of type: country, person,
nationality, location, organization, and city, to create AfriSQuAD2. Using the
perturbed test set, we evaluate the robustness of three popular MRC models. We
find that compared to base models, large models perform well comparatively on
novel entities. Furthermore, our analysis indicates that entity type person
highly challenges the MRC models' performance.

本研究旨在探究机器阅读理解模型对命名实体重命名的鲁棒性，尤其是对低资源地区如非洲的实体。通过创建一个被重命名的数据集 AfriSQuAD2 来测试模型的鲁棒性，并发现大模型相对于基础模型表现更好，而人类实体类型则高度挑战 MRC 模型的性能。