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 https://github.com/owkin/imilia.

翻译：随着炎症性肠病（IBD）的治疗目标转向组织学缓解，微观炎症的准确评估对于疾病活动性和治疗反应的判断日益重要。本研究提出IMILIA（Interpretable Multiple Instance Learning for Inflammation Analysis），一种端到端框架，用于预测经苏木精-伊红（H&E）染色的IBD数字化切片中的炎症存在，并自动计算驱动预测的组织区域特征标记物。IMILIA由炎症预测模块（包含多示例学习模型）和可解释性模块构成，后者分为两个部分：用于细胞实例检测、分割与分类的HistoPLUS模块，以及用于上皮分割的EpiSeg模块。在发现队列中，IMILIA的交叉验证ROC-AUC达到0.83；在两个外部验证队列中，ROC-AUC分别达到0.99和0.84。可解释性模块提供了生物学一致的解释：预测分数较高的图像块显示免疫细胞（淋巴细胞、浆细胞、中性粒细胞和嗜酸性粒细胞）密度增加，而低分图像块主要包含正常上皮细胞。值得注意的是，这些模式在所有数据集中均保持一致。部分复现公开数据集IBDColEpi结果的代码与模型可在https://github.com/owkin/imilia获取。