Forootan Mojgan, Rajabnia Mohsen, Mafi Ahmad R, Tehrani Hamed Azhdari, Ghadirzadeh Erfan, Setayeshfar Mahziar, Ghaffari Zahra, Tashakoripour Mohammad, Zali Mohammad Reza, Bolhasani Hamidreza
Gastroenterology and Liver Disease Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Alborz University of Medical Sciences, Alborz, Iran.
BMC Res Notes. 2024 Dec 28;17(1):393. doi: 10.1186/s13104-024-07062-6.
This dataset contains demographic, morphological and pathological data, endoscopic images and videos of 191 patients with colorectal polyps. Morphological data is included based on the latest international gastroenterology classification references such as Paris, Pit and JNET classification. Pathological data includes the diagnosis of the polyps including Tubular, Villous, Tubulovillous, Hyperplastic, Serrated, Inflammatory and Adenocarcinoma with Dysplasia Grade & Differentiation.Objectives: Today the most important challenge of developing accurate algorithms for medical prediction, detection, diagnosis, treatment and prognosis is data. ERCPMP is an Endoscopic Image and Video Dataset for Recognition of Colorectal Polyps Morphology and Pathology. This dataset can be used for developing deep learning algorithms for polyps detection, classification, and segmentation.Data description: Images were captured with Olympus colonoscope and are presented in RGB format, JPG type with the resolution of 368 * 256 pixels and 96 dpi. The name of each file (image or video) includes pathological diagnosis, grade and JNet classification of the related polyp.
该数据集包含191例大肠息肉患者的人口统计学、形态学和病理学数据、内镜图像及视频。形态学数据是依据最新的国际胃肠病学分类参考文献（如巴黎分类、皮氏分类和日本消化内镜学会分类）收录的。病理学数据包括息肉的诊断结果，如管状、绒毛状、管状绒毛状、增生性、锯齿状、炎性以及伴有发育异常分级和分化的腺癌。目标：如今，为医学预测、检测、诊断、治疗及预后开发精确算法面临的最重要挑战是数据。ERCPMP是一个用于识别大肠息肉形态学和病理学的内镜图像及视频数据集。该数据集可用于开发针对息肉检测、分类及分割的深度学习算法。数据描述：图像由奥林巴斯结肠镜采集，以RGB格式呈现，为JPG类型，分辨率为368×256像素，96 dpi。每个文件（图像或视频）的名称包含相关息肉的病理诊断、分级及日本消化内镜学会分类。
