@inbook{101275,
  keywords = {Teledermatology, Telemedicine, Dataset, Sub-Saharan Africa},
  author = {Gottfrois P and Gröger F and Andriambololoniaina FH and Amruthalingam L and Gonzalez-Jimenez A and Hsu C and Kessy A and Lionetti S and Mavura D and Ng’ambi W and Ngongonda DF and Pouly M and Rakotoarisaona MF and Rapelanoro Rabenja F and Traoré I and Navarini AA},
  title = {PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa},
  abstract = {<p>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/.</p>
},
  year = {2024},
  journal = {Lecture Notes in Computer Science},
  pages = {703-712},
  publisher = {Springer Nature Switzerland},
  issn = {0302-9743, 1611-3349},
  isbn = {9783031723834},
  url = {https://papers.miccai.org/miccai-2024/paper/3722_paper.pdf},
  doi = {10.1007/978-3-031-72384-1_66},
  language = {ENG},
}