UNN-LC High-Resolution Histopathological Lung Tissue Patch Dataset

The UNN-LC High-Resolution Histopathological Lung Tissue Patch Dataset is a collection of image patches designed for computational prognostic evaluation of lung cancer. Compiled from a subset of 194 whole-slide images (WSIs) from the University Hospital of North Norway, this dataset provides a comprehensive representation of various lung tissue conditions. Each 768 x 768 pixel patch contributes to a detailed analysis of tissue morphology. The dataset was annotated by an oncologist (Thomas Kilvær) and a pathologist (Stig Dalen) with a concerted effort to minimize selection and labeling biases. Specifically, patches with predominantly cancer cells, including tumor-infiltrating lymphocytes, were annotated by Stig Dalen. Thomas Kilvær provided annotations for patches representing normal lung tissue. The combined efforts of Stig Dalen and Thomas Kilvær resulted in the annotations for the reactive stroma with tertiary lymphoid structures and necrosis areas data. Annotations were acquired using QuPath software and a custom-developed annotation tool. The dataset categorizes patches into four classes: necrosis, tumor, stroma_tls, and normal_lung. The necrosis class includes patches of tissue associated with tumor regions, while the normal lung class represents areas of healthy lung tissue, inclusive of stromal components. The stroma_tls class is characterized by patches of reactive stroma with dense tissue and lymphocyte aggregates. The tumor tissue class comprises patches with a predominant presence of tumor content and may also include areas with tumor-infiltrating lymphocytes (TILs). For those interested in further expanding the scope and improving the balance of classes within the dataset, additional patches from the LC25000 dataset can be integrated for a more diverse representation of tissue conditions. This approach can enhance the robustness of computational models developed using this data. The dataset is divided into training and testing sets to facilitate and promote reproducibility in the development and validation of vision models. The training set includes a selection of patches from each class, while the testing set is composed of the remaining patches to ensure a comprehensive assessment of model performance.UNN-LC 高分辨率肺组织病理图像块数据集（UNN-LC High-Resolution Histopathological Lung Tissue Patch Dataset）是专为肺癌计算预后评估设计的图像块集合。该数据集源自挪威北部大学医院194张全视野数字切片（whole-slide images, WSIs）的子集，全面呈现了多种肺组织状态的特征。每个图像块尺寸为768×768像素，可用于精细的组织形态学分析。该数据集由肿瘤学家Thomas Kilvær与病理学家Stig Dalen共同标注，旨在最大限度减少选择偏差与标注偏差。具体分工如下：Stig Dalen负责标注以癌细胞为主的图像块，包括肿瘤浸润淋巴细胞（tumor-infiltrating lymphocytes, TILs）区域；Thomas Kilvær负责标注代表正常肺组织的图像块；而带有三级淋巴结构的反应性基质及坏死区域的图像块标注，则由二人协作完成。标注工作借助QuPath软件与自研标注工具完成。该数据集将图像块划分为四个类别：坏死（necrosis）、肿瘤（tumor）、基质-三级淋巴结构（stroma_tls）以及正常肺组织（normal_lung）。其中，坏死类别包含与肿瘤区域相关的组织图像块；正常肺组织类别代表健康肺组织区域，涵盖间质成分；基质-三级淋巴结构类别的特征为带有致密组织与淋巴细胞聚集的反应性基质图像块；肿瘤组织类别则包含以肿瘤成分为主的图像块，也可涵盖肿瘤浸润淋巴细胞区域。若希望进一步拓展数据集范围并优化类别分布平衡性，可集成LC25000数据集中的额外图像块，以实现更丰富的组织状态表征，进而提升基于该数据开发的计算模型的鲁棒性。本数据集已划分为训练集与测试集，以推动视觉模型开发与验证流程的可重复性。训练集包含每类图像块的精选子集，测试集则由剩余图像块构成，以此确保对模型性能的全面评估。