PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones

TL;DR

This paper introduces PAD-UFES-20, a comprehensive dataset of clinical smartphone images and patient data for skin lesion analysis, aiming to support the development of AI tools for skin cancer detection.

Contribution

The paper provides a new publicly available dataset with clinical images and patient data, filling a gap in resources for skin lesion analysis beyond dermoscopy images.

Findings

Dataset includes 1,373 patients and 1,641 lesions.

Contains biopsy-proven cases, including all skin cancers.

Aims to facilitate AI research in skin cancer detection.

Abstract

Over the past few years, different computer-aided diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to design them. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 22 features. The dataset consists of 1,373 patients, 1,641 skin lesions, and 2,298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to aid future research and the development of new tools to assist clinicians to detect skin cancer.