Abstract
As the therapeutic target for Inflammatory Bowel Disease (IBD) shifts toward histologic remission, the accurate assessment of microscopic inflammation has become increasingly central for evaluating disease activity and response to treatment. In this work, we introduce IMILIA (Interpretable Multiple Instance Learning for Inflammation Analysis), an end-to-end framework designed for the prediction of inflammation presence in IBD digitized slides stained with hematoxylin and eosin (H&E), followed by the automated computation of markers characterizing tissue regions driving the predictions. IMILIA is composed of an inflammation prediction module, consisting of a Multiple Instance Learning (MIL) model, and an interpretability module, divided in two blocks: HistoPLUS, for cell instance detection, segmentation and classification; and EpiSeg, for epithelium segmentation. IMILIA achieves a cross-validation ROC-AUC of 0.83 on the discovery cohort, and a ROC-AUC of 0.99 and 0.84 on two external validation cohorts. The interpretability module yields biologically consistent insights: tiles with higher predicted scores show increased densities of immune cells (lymphocytes, plasmocytes, neutrophils and eosinophils), whereas lower-scored tiles predominantly contain normal epithelial cells. Notably, these patterns were consistent across all datasets. Code and models to partially replicate the results on the public IBDColEpi dataset can be found at this https URL.
Abstract (translated)
随着炎症性肠病（IBD）的治疗目标转向组织学缓解，对微观炎症的准确评估已成为评价疾病活动性和治疗反应的关键。本文介绍了IMILIA（可解释的多重实例学习炎症分析），这是一种端到端框架，用于预测经苏木精和伊红（H&E）染色后数字化的IBD切片中的炎症存在，并随后自动计算表征驱动预测的组织区域标记物。 IMILIA由一个炎症预测模块组成，该模块包含一个多实例学习（MIL）模型，以及解释性模块，分为两个部分：HistoPLUS用于细胞实例检测、分割和分类；EpiSeg用于上皮层分割。在发现队列中，IMILIA实现了交叉验证ROC-AUC为0.83，在两个外部验证队列中的ROC-AUC分别为0.99和0.84。 解释性模块提供了生物学一致的见解：预测得分较高的切片显示出免疫细胞（淋巴细胞、浆细胞、中性粒细胞和嗜酸性粒细胞）密度增加，而得分较低的切片主要包含正常上皮细胞。值得注意的是，在所有数据集中这些模式是一致的。可以在以下网址找到用于部分复制在公开IBDColEpi数据集上的结果代码和模型：[此链接](https://this-URL.com)（请将"this https URL"替换为实际的代码和模型存放地址）。
URL
https://arxiv.org/abs/2512.13440
