Computer-based analysis of Wireless Capsule Endoscopy (WCE) is crucial. However, a medically annotated WCE dataset for training and evaluation of automatic classification, detection, and segmentation of bleeding and non-bleeding frames is currently lacking. The present work focused on development of a medically annotated WCE dataset called WCEbleedGen for automatic classification, detection, and segmentation of bleeding and non-bleeding frames. It comprises 2,618 WCE bleeding and non-bleeding frames which were collected from various internet resources and existing WCE datasets. A comprehensive benchmarking and evaluation of the developed dataset was done using nine classification-based, three detection-based, and three segmentation-based deep learning models. The dataset is of high-quality, is class-balanced and contains single and multiple bleeding sites. Overall, our standard benchmark results show that Visual Geometric Group (VGG) 19, You Only Look Once version 8 nano (YOLOv8n), and Link network (Linknet) performed best in automatic classification, detection, and segmentation-based evaluations, respectively. Automatic bleeding diagnosis is crucial for WCE video interpretations. This diverse dataset will aid in developing of real-time, multi-task learning-based innovative solutions for automatic bleeding diagnosis in WCE. The dataset and code are publicly available at https://zenodo.org/records/10156571 and https://github.com/misahub2023/Benchmarking-Codes-of-the-WCEBleedGen-dataset.

翻译：无线胶囊内镜（WCE）的计算机辅助分析至关重要。然而，目前缺乏用于训练和评估出血与非出血帧自动分类、检测及分割的医学标注WCE数据集。本研究致力于构建一个名为WCEbleedGen的医学标注WCE数据集，用于出血与非出血帧的自动分类、检测与分割。该数据集包含2,618张WCE出血与非出血帧图像，采集自多种网络资源及现有WCE数据集。通过九种基于分类、三种基于检测及三种基于分割的深度学习模型，对构建的数据集进行了全面的基准测试与评估。本数据集质量高、类别均衡，并包含单发与多发出血病灶。总体而言，标准基准测试结果表明：视觉几何组（VGG）19、You Only Look Once version 8 nano（YOLOv8n）及Link网络（Linknet）分别在基于分类、检测与分割的自动评估中表现最佳。自动出血诊断对WCE视频判读至关重要。本多样化数据集将有助于开发基于实时多任务学习的创新解决方案，以实现WCE中的自动出血诊断。数据集与代码已公开于https://zenodo.org/records/10156571 与 https://github.com/misahub2023/Benchmarking-Codes-of-the-WCEBleedGen-dataset。