标题： ADPv2：一种分层组织学组织类型注释的数据集，用于结直肠疾病潜在生物标志物的发现 显示英文标题

标题： ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease

摘要： 计算病理学（CoPath）利用组织病理学图像来提高临床病理学中的诊断精度和可重复性。 然而，由于需要大量专业知识和高昂的标注成本，目前公开可用的CoPath数据集在细粒度水平上带有广泛组织学组织类型（HTT）分类的数据集仍然很少。 现有的数据集，如数字病理学图谱（ADP），通过提供针对多个器官的多样化HTT标注来解决这一问题，但限制了对特定器官疾病进行深入研究的能力。 在此基础上，我们引入了ADPv2，一个专注于胃肠道组织病理学的新数据集。 我们的数据集包含从健康结肠活检切片中提取的20,004个图像块，根据32种不同HTT的三级层次分类进行标注。 此外，我们在我们的ADPv2数据集上按照两阶段训练程序训练了一个多标签表示学习模型。 我们采用VMamba架构，在结肠HTT的多标签分类中实现了平均精度（mAP）为0.88。 最后，我们通过分析模型在不同结肠疾病影响组织上的预测行为，展示了我们的数据集在潜在生物标志物发现方面的器官特异性深入研究能力，这揭示了证实结肠癌发展的两种病理途径的统计模式。 我们的数据集可在https://zenodo.org/records/15307021 公开获取 显示英文摘要

摘要： Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CoPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce due to the significant expertise and high annotation costs required. Existing datasets, such as the Atlas of Digital Pathology (ADP), address this by offering diverse HTT annotations generalized to multiple organs, but limit the capability for in-depth studies on specific organ diseases. Building upon this foundation, we introduce ADPv2, a novel dataset focused on gastrointestinal histopathology. Our dataset comprises 20,004 image patches derived from healthy colon biopsy slides, annotated according to a hierarchical taxonomy of 32 distinct HTTs of 3 levels. Furthermore, we train a multilabel representation learning model following a two-stage training procedure on our ADPv2 dataset. We leverage the VMamba architecture and achieving a mean average precision (mAP) of 0.88 in multilabel classification of colon HTTs. Finally, we show that our dataset is capable of an organ-specific in-depth study for potential biomarker discovery by analyzing the model's prediction behavior on tissues affected by different colon diseases, which reveals statistical patterns that confirm the two pathological pathways of colon cancer development. Our dataset is publicly available at https://zenodo.org/records/15307021