用滑动操作加速病理图像标注，支持移动端高效协作。
SWAN -- Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces
- 通过滑动手势实现图像块快速分类，支持多平台使用。
- 4名病理科医生标注600张切片，一致性达86.5%~93.7%。
- 移动端友好，提升标注效率，适合大规模病理数据构建。
大规模病理图像数据集的标注仍是开发临床相关深度学习模型的主要瓶颈，如核分裂象分类。传统的文件夹式标注流程通常耗时、易疲劳且难以扩展。为此，我们提出SWipeable ANnotations（SWAN），一个开源、MIT许可的Web应用，通过滑动手势实现直观的图像块分类。SWAN支持桌面与移动平台，具备实时元数据记录功能，并可灵活将滑动手势映射至类别标签。在一项包含4名病理科医生标注600个核分裂象图像块的试点研究中，与传统文件夹排序工作流对比，SWAN在区分异常与正常核分裂象任务中，配对一致率介于86.52%至93.68%之间（Cohen's Kappa = 0.61–0.80），而传统方法为86.98%至91.32%（Cohen's Kappa = 0.63–0.75），表明标注一致性高且性能相当。参与者普遍认为该工具易用，尤其赞赏移动端标注能力。结果表明，SWAN可在保持标注质量的同时显著加速图像标注，为传统流程提供可扩展、用户友好的替代方案。
原文摘要 · Abstract (English)
The annotation of large scale histopathology image datasets remains a major bottleneck in developing robust deep learning models for clinically relevant tasks, such as mitotic figure classification. Folder-based annotation workflows are usually slow, fatiguing, and difficult to scale. To address these challenges, we introduce SWipeable ANnotations (SWAN), an open-source, MIT-licensed web application that enables intuitive image patch classification using a swiping gesture. SWAN supports both desktop and mobile platforms, offers real-time metadata capture, and allows flexible mapping of swipe gestures to class labels. In a pilot study with four pathologists annotating 600 mitotic figure image patches, we compared SWAN against a traditional folder-sorting workflow. SWAN enabled rapid annotations with pairwise percent agreement ranging from 86.52% to 93.68% (Cohen's Kappa = 0.61-0.80), while for the folder-based method, the pairwise percent agreement ranged from 86.98% to 91.32% (Cohen's Kappa = 0.63-0.75) for the task of classifying atypical versus normal mitotic figures, demonstrating high consistency between annotators and comparable performance. Participants rated the tool as highly usable and appreciated the ability to annotate on mobile devices. These results suggest that SWAN can accelerate image annotation while maintaining annotation quality, offering a scalable and user-friendly alternative to conventional workflows.
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