The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection

TL;DR

The iToBoS dataset provides a large collection of skin region images from 3D total body photos, including contextual skin information and annotations, to improve lesion detection and skin cancer diagnosis.

Contribution

This paper introduces the iToBoS dataset, a novel large-scale skin region image dataset with contextual information for enhanced lesion detection.

Findings

Provides 16,954 annotated skin region images from 100 participants

Includes metadata like anatomical location, age, and sun damage

Facilitates training and benchmarking of skin lesion detection algorithms

Abstract

Artificial intelligence has significantly advanced skin cancer diagnosis by enabling rapid and accurate detection of malignant lesions. In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the center of the image. While these lesion-centric datasets have been fundamental for developing diagnostic algorithms, they lack the context of the surrounding skin, which is critical for improving lesion detection. The iToBoS dataset was created to address this challenge. It includes 16,954 images of skin regions from 100 participants, captured using 3D total body photography. Each image roughly corresponds to a $7 \times 9$ cm section of skin with all suspicious lesions annotated using bounding boxes. Additionally, the dataset provides metadata such as anatomical location, age group, and sun damage score for each image. This dataset aims to facilitate training and benchmarking of algorithms, with the goal of enabling early detection of skin cancer and deployment of this technology in non-clinical environments.