病理分析终于有了可规模化的 AI 方案——Atlas H&E-TME 在 H&E 图像上达到专家级精度，做肿瘤微环境研究和临床转化的团队可以直接用上这套定量工具。
Atlas H&E-TME 是一个基于 Atlas 病理基础模型的 AI 系统，能够从 H&E 染色的全切片图像中预测组织质量、区域和细胞类型，每张切片输出超过 4500 个细胞级定量指标。研究团队提出了双重验证框架：一方面利用免疫组化（IHC）信息构建多病理学家共识，作为分子层面的金标准；另一方面在超过 20 万条高置信度病理学家标注上测试，覆盖 8 种癌症类型、1500+ 病例。结果显示，Atlas H&E-TME 在 H&E 图像上的表现与病理学家相当甚至更优，且泛化能力强。这一系统将最普遍的 H&E 切片转化为可扩展的定量工具，为下一代组织生物标志物研究奠定基础。
Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
Hematoxylin and eosin (H&E) staining is the cornerstone of histopathology, yet scalable, quantitative analysis of H&E whole-slide images (WSIs) remains a central challenge in computational pathology. We present Atlas H&E-TME, an AI-based system built on the Atlas family of pathology foundation models that predicts tissue quality, tissue region, and cell type labels across multiple cancer types, yielding over 4,500 quantitative readouts per slide at cell-level resolution. A key challenge to validating such systems is overcoming morphological ambiguity inherent to H&E-only ground truth and the limited scalability of more informed references drawing on modalities such as immunohistochemistry (IHC). We address this with a dual validation framework combining biologically grounded depth with technical and morphological breadth. For depth, we propose an IHC-informed multi-pathologist consensus protocol that substantially improves inter-rater agreement over conventional H&E-only annotation. This yields a molecularly grounded reference against which we compare Atlas H&E-TME and pathologists working from H&E alone. For breadth, we benchmark Atlas H&E-TME on over 200,000 high-confidence H&E-only pathologist annotations across 1,500+ cases spanning eight cancer types and their most common metastatic sites, with subtypes covering >90% of clinical cases per cancer type, drawn from 25+ sources and 8+ scanner models. Benchmarked against the IHC-informed consensus, Atlas H&E-TME matches or exceeds pathologist H&E-only performance and generalizes consistently and robustly across this broad morphological and technical scope. In doing so, Atlas H&E-TME turns the H&E slide -- the most ubiquitous data in pathology -- into a scalable, quantitative window into the tumor and its microenvironment, laying a foundation for the next generation of tissue-based biomarkers in translational and clinical research.