SWAN -- Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces

TL;DR

SWAN is a web application that simplifies histopathology image annotation using swipe gestures, enabling faster, more scalable, and user-friendly annotation on both desktop and mobile devices, with high consistency and usability.

Contribution

This paper introduces SWAN, a novel swipe-based annotation tool that improves speed and scalability of histopathology image labeling compared to traditional folder-based methods.

Findings

SWAN achieved high inter-annotator agreement (86.52%-93.68%)

Participants rated SWAN as highly usable and mobile-friendly

SWAN's annotation speed was superior to traditional workflows

Abstract

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.