Abstract
Cell detection, segmentation and classification are essential for analyzing tumor microenvironments (TME) on hematoxylin and eosin (H&E) slides. Existing methods suffer from poor performance on understudied cell types (rare or not present in public datasets) and limited cross-domain generalization. To address these shortcomings, we introduce HistoPLUS, a state-of-the-art model for cell analysis, trained on a novel curated pan-cancer dataset of 108,722 nuclei covering 13 cell types. In external validation across 4 independent cohorts, HistoPLUS outperforms current state-of-the-art models in detection quality by 5.2% and overall F1 classification score by 23.7%, while using 5x fewer parameters. Notably, HistoPLUS unlocks the study of 7 understudied cell types and brings significant improvements on 8 of 13 cell types. Moreover, we show that HistoPLUS robustly transfers to two oncology indications unseen during training. To support broader TME biomarker research, we release the model weights and inference code at this https URL.
Abstract (translated)
细胞检测、分割和分类对于分析肿瘤微环境（TME）在苏木精-伊红（H&E）切片上的情况至关重要。现有的方法在处理研究不足的细胞类型（罕见或未出现在公共数据集中的细胞类型）以及跨领域泛化方面表现不佳。为了解决这些问题，我们引入了HistoPLUS，这是一种先进的细胞分析模型，它是在一个新的精心策划的、包含108,722个核（覆盖13种细胞类型）的泛癌症数据集上训练出来的。在外部验证中，HistoPLUS在四个独立队列中的检测质量比目前最先进的模型高出5.2%，整体F1分类得分提高了23.7%，同时使用的参数减少了5倍。值得注意的是，HistoPLUS解锁了对七种研究不足的细胞类型的研究，并且在这十三种细胞类型中有八种取得了显著改进。此外，我们还展示了HistoPLUS能够稳健地转移到训练期间未见过的两种肿瘤学指征上。为了支持更广泛的TME生物标志物研究，我们在[这里](https://example.com)发布了模型权重和推理代码。
URL
https://arxiv.org/abs/2508.09926
