PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Gottfrois, Philippe Gröger, Fabian Andriambololoniaina, Faly Herizo Amruthalingam, Ludovic Gonzalez-Jimenez, Alvaro Hsu, Christophe Kessy, Agnes Lionetti, Simone Mavura, Daudi Ng'ambi, Wingston Ngongonda, Dingase Faith Pouly, Marc Rakotoarisaona, Mendrika Fifaliana Rabenja, Fahafahantsoa Rapelanoro Traoré, Ibrahima Navarini, Alexander A. |
| author_facet | Gottfrois, Philippe Gröger, Fabian Andriambololoniaina, Faly Herizo Amruthalingam, Ludovic Gonzalez-Jimenez, Alvaro Hsu, Christophe Kessy, Agnes Lionetti, Simone Mavura, Daudi Ng'ambi, Wingston Ngongonda, Dingase Faith Pouly, Marc Rakotoarisaona, Mendrika Fifaliana Rabenja, Fahafahantsoa Rapelanoro Traoré, Ibrahima Navarini, Alexander A. |
| contents | 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/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_04584 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa Gottfrois, Philippe Gröger, Fabian Andriambololoniaina, Faly Herizo Amruthalingam, Ludovic Gonzalez-Jimenez, Alvaro Hsu, Christophe Kessy, Agnes Lionetti, Simone Mavura, Daudi Ng'ambi, Wingston Ngongonda, Dingase Faith Pouly, Marc Rakotoarisaona, Mendrika Fifaliana Rabenja, Fahafahantsoa Rapelanoro Traoré, Ibrahima Navarini, Alexander A. Computer Vision and Pattern Recognition 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/. |
| title | PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.04584 |