PASSION for Dermatology

MICCAI 2024

Philippe Gottfrois^7,8, Fabian Gröger^5,8, Faly Herizo Andriambololoniaina⁴, Ludovic Amruthalingam⁵, Alvaro Gonzalez-Jimenez⁸, Christophe Hsu⁷, Agnes Kessy⁶, Simone Lionetti⁵, Daudi Mavura⁶, Wingston Ng'ambi², Dingase Faith Ngongonda¹, Marc Pouly⁵, Mendrika Fifaliana Rakotoarisaona⁴, Fahafahantsoa Rapelanoro Rabenja⁴, Ibrahima Traoré³, Alexander Navarini^7,8

¹ Bwaila Hospital Department of Dermatology,
² Health Economics and Policy Unit, Department of Health Systems and Policy, Kamuzu University of Health Sciences, Lilongwe, Malawi
³ Clinique dermatologique, Konakry,
⁴ Laboratoire d’Accueil et de Recherche en santé publique spécialisé en TIC,
⁵ Lucerne University of Applied Sciences and Arts,
⁶ Regional Dermatology Training Center, Moshi,
⁷ University Hospital Basel,
⁸ University of Basel,

Abstract

Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatric population suffering from largely untreated skin conditions. The integration of AI into healthcare sparks significant hope for treatment accessibility, especially through the development of AI-supported teledermatology. Current AI models are predominantly trained on white-skinned patients and do not generalize well enough to pigmented patients. The PASSION project aims to address this issue by collecting images of skin diseases in Sub-Saharan countries with the aim of open-sourcing this data. This dataset is the first of its kind, consisting of 1,653 patients for a total of 4,901 images. The images are representative of telemedicine settings and encompass the most common paediatric conditions: eczema, fungals, scabies, and impetigo. We also provide a baseline machine learning model trained on the dataset and a detailed performance analysis for the subpopulations represented in the dataset. The project website can be found at https://passionderm.github.io/.

Dataset

Sample Images

Dataset Usage Agreement

This dataset is provided under the PASSION data public license.

Key Points:

Non-Commercial Use Only: You may use the dataset for non-commercial purposes only. Commercial use is strictly prohibited.

Attribution Required:If sharing or adapting the data, credit the original creator.

Prohibition on Re-identification: You must not attempt to re-identify any individuals within the dataset. Any effort to re-identify individuals is strictly prohibited.

Sharing Restrictions:

Direct Download Link: You must not share the direct download link to the dataset.
Website Link Only: If you wish to share the dataset, you must share the link to the website hosting the dataset, not the direct download link.
Reposting Prohibition: You must not repost the dataset on any other data hosting platforms or websites.

By using this dataset, you acknowledge that you have read and agree to abide by these terms. Any violation of these terms may result in legal action and the revocation of your right to use the dataset.

BibTeX

@InProceedings{10.1007/978-3-031-72384-1_66, author = { Gottfrois, Philippe and Gröger, Fabian and Andriambololoniaina, Faly Herizo and Amruthalingam, Ludovic and Gonzalez-Jimenez, Alvaro and Hsu, Christophe and Kessy, Agnes and Lionetti, Simone and Mavura, Daudi and Ng'ambi, Wingston and Ngongonda, Dingase Faith and Pouly, Marc and Rakotoarisaona, Mendrika Fifaliana and Rapelanoro Rabenja, Fahafahantsoa and Traoré, Ibrahima and Navarini, Alexander A. }, title = "PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa", booktitle = "Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024", year = "2024", publisher = "Springer Nature Switzerland", address = "Cham", pages = "703--712", isbn = "978-3-031-72384-1" }