The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Inflammatory processes in the placenta have short-term, and long-term consequences for offspring health. Digital pathology and machine learning can play an important role in understanding placental inflammation, and there have been very few investigations into methods for predicting and understanding Maternal Inflammatory Response (MIR). This work intends to investigate the potential of using machine learning to understand MIR based on whole slide images (WSI), and establish early benchmarks. To that end, we use Multiple Instance Learning framework with 3 feature extractors: ImageNet-based EfficientNet-v2s, and 2 histopathology foundation models, UNI and Phikon to investigate predictability of MIR stage from histopathology WSIs. We also interpret predictions from these models using the learned attention maps from these models. We also use the MIL framework for predicting white blood cells count (WBC) and maximum fever temperature ($T_{max}$). Attention-based MIL models are able to classify MIR with a balanced accuracy of up to 88.5% with a Cohen's Kappa ($\kappa$) of up to 0.772. Furthermore, we found that the pathology foundation models (UNI and Phikon) are both able to achieve higher performance with balanced accuracy and $\kappa$, compared to ImageNet-based feature extractor (EfficientNet-v2s). For WBC and $T_{max}$ prediction, we found mild correlation between actual values and those predicted from histopathology WSIs. We used MIL framework for predicting MIR stage from WSIs, and compared effectiveness of foundation models as feature extractors, with that of an ImageNet-based model. We further investigated model failure cases and found them to be either edge cases prone to interobserver variability, examples of pathologist's overreach, or mislabeled due to processing errors.

翻译：胎盘在妊娠、分娩及生产过程中构成抵御感染的关键屏障。胎盘内的炎症过程对子代健康具有短期及长期影响。数字病理学与机器学习在理解胎盘炎症方面可发挥重要作用，但目前针对母体炎症反应（MIR）预测与理解方法的研究极为有限。本研究旨在探索利用机器学习基于全切片图像（WSI）理解MIR的潜力，并建立早期基准。为此，我们采用多示例学习框架，结合三种特征提取器：基于ImageNet的EfficientNet-v2s，以及两种组织病理学基础模型UNI和Phikon，以探究从组织病理学WSI预测MIR分期的可行性。同时，我们利用这些模型学习得到的注意力图对预测结果进行解释。此外，我们还应用MIL框架预测白细胞计数（WBC）与最高发热体温（$T_{max}$）。基于注意力的MIL模型对MIR的分类平衡准确率最高可达88.5%，科恩卡帕系数（$\kappa$）最高达0.772。进一步研究发现，与基于ImageNet的特征提取器（EfficientNet-v2s）相比，病理学基础模型（UNI和Phikon）在平衡准确率与$\kappa$值上均能取得更高性能。针对WBC与$T_{max}$预测，我们发现实际值与基于组织病理学WSI的预测值之间存在轻度相关性。本研究采用MIL框架从WSI预测MIR分期，并比较了基础模型与基于ImageNet模型作为特征提取器的效能。我们进一步分析了模型失败案例，发现其成因包括易受观察者间差异影响的边缘案例、病理学家过度判读的实例，或因处理错误导致的标注失误。