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

• URL: http://arxiv.org/abs/2501.18270v1
• Date: Thu, 30 Jan 2025 11:10:44 GMT
• Title: The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection
• Authors: Anup Saha, Joseph Adeola, Nuria Ferrera, Adam Mothershaw, Gisele Rezze, Séraphin Gaborit, Brian D'Alessandro, James Hudson, Gyula Szabó, Balazs Pataki, Hayat Rajani, Sana Nazari, Hassan Hayat, Clare Primiero, H. Peter Soyer, Josep Malvehy, Rafael Garcia,
• Abstract summary: iToBoS dataset 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. 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.
• Score: 0.35348820263620895
• License:
• 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.

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