IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset

TL;DR

This paper introduces ISIC MultiAnnot++, the largest publicly available multi-annotator dermoscopic skin lesion segmentation dataset, enabling research on annotation variability and improving AI-based skin lesion analysis.

Contribution

The creation of a large-scale, multi-annotator dermoscopic skin lesion segmentation dataset with detailed metadata and analysis, filling a critical gap in publicly available resources.

Findings

Largest multi-annotator skin lesion segmentation dataset to date

Includes metadata on annotator skill and tools

Provides curated data partitions and consensus masks

Abstract

Multi-annotator medical image segmentation is an important research problem, but requires annotated datasets that are expensive to collect. Dermoscopic skin lesion imaging allows human experts and AI systems to observe morphological structures otherwise not discernable from regular clinical photographs. However, currently there are no large-scale publicly available multi-annotator skin lesion segmentation (SLS) datasets with annotator-labels for dermoscopic skin lesion imaging. We introduce ISIC MultiAnnot++, a large public multi-annotator skin lesion segmentation dataset for images from the ISIC Archive. The final dataset contains 17,684 segmentation masks spanning 14,967 dermoscopic images, where 2,394 dermoscopic images have 2-5 segmentations per image, making it the largest publicly available SLS dataset. Further, metadata about the segmentation, including the annotators' skill level and segmentation tool, is included, enabling research on topics such as annotator-specific preference modeling for segmentation and annotator metadata analysis. We provide an analysis on the characteristics of this dataset, curated data partitions, and consensus segmentation masks.