PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa

• URL: http://arxiv.org/abs/2411.04584v1
• Date: Thu, 07 Nov 2024 10:11:37 GMT
• Title: PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa
• Authors: Philippe Gottfrois, Fabian Gröger, Faly Herizo Andriambololoniaina, Ludovic Amruthalingam, Alvaro Gonzalez-Jimenez, Christophe Hsu, Agnes Kessy, Simone Lionetti, Daudi Mavura, Wingston Ng'ambi, Dingase Faith Ngongonda, Marc Pouly, Mendrika Fifaliana Rakotoarisaona, Fahafahantsoa Rapelanoro Rabenja, Ibrahima Traoré, Alexander A. Navarini,
• Abstract summary: 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 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.
• Score: 29.405369900938393
• License:
• 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/.

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