RCdpia：基于病理学家的肾癌数字病理图像标注数据集
RCdpia: A Renal Carcinoma Digital Pathology Image Annotation dataset based on pathologists
AI总结：
针对肾细胞癌异质性，构建了基于病理学家标注的RCdpia数据集，含887例三类肾癌病例，并用Resnet验证，以支持更精准的数字病理AI模型开发。
摘要：
由于肾细胞癌肿瘤的异质性，数字病理切片数据的标注对于人工智能模型的正确诊断至关重要。这一过程不仅有助于更深入地理解肾细胞癌的异质性，还旨在最小化数据中的噪声，以实现更准确的研究。为增强数据的适用性，我们聘请了两位病理学家，从癌症基因组图谱计划（TCGA）数据库中精心策划、筛选并标注了一个肾癌病理图像数据集。随后，开发了一个Resnet模型，以针对来自浙江大学附属第一医院的额外数据集验证该标注数据集。基于这些结果，我们精心编制了TCGA数字病理数据集，对肿瘤区域和邻近区域进行了独立标注（RCdpia），其中包括109例肾嫌色细胞癌、486例肾透明细胞癌和292例肾乳头状细胞癌病例。该数据集现已在http://39.171.241.18:8888/RCdpia/公开访问。此外，模型分析揭示了将同一模型应用于不同中心的数据集时预测结果的显著差异。利用RCdpia，我们现在可以开发更精确的数字病理人工智能模型，用于归一化、分类和分割等任务。这些进展凸显了在数字病理领域实现更细致和准确的人工智能应用的潜力。
英文摘要：
The annotation of digital pathological slide data for renal cell carcinoma is of paramount importance for correct diagnosis of artificial intelligence models due to the heterogeneous nature of the tumor. This process not only facilitates a deeper understanding of renal cell cancer heterogeneity but also aims to minimize noise in the data for more accurate studies. To enhance the applicability of the data, two pathologists were enlisted to meticulously curate, screen, and label a kidney cancer pathology image dataset from The Cancer Genome Atlas Program (TCGA) database. Subsequently, a Resnet model was developed to validate the annotated dataset against an additional dataset from the First Affiliated Hospital of Zhejiang University. Based on these results, we have meticulously compiled the TCGA digital pathological dataset with independent labeling of tumor regions and adjacent areas (RCdpia), which includes 109 cases of kidney chromophobe cell carcinoma, 486 cases of kidney clear cell carcinoma, and 292 cases of kidney papillary cell carcinoma. This dataset is now publicly accessible at http://39.171.241.18:8888/RCdpia/. Furthermore, model analysis has revealed significant discrepancies in predictive outcomes when applying the same model to datasets from different centers. Leveraging the RCdpia, we can now develop more precise digital pathology artificial intelligence models for tasks such as normalization, classification, and segmentation. These advancements underscore the potential for more nuanced and accurate AI applications in the field of digital pathology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
