mlsanityData to 7 Oct 2026

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

arXiv 2411.04584 ↗First posted 7 Nov 2024cs.CVcited by 5

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Abstract (arXiv)

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/.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

9 of 32 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Cyclical Learning Rates for Training Neural Networkscited by 627
  3. 2019BCN20000: Dermoscopic Lesions in the Wildcited by 224
  4. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 184
  5. 2020A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Contextcited by 146
  6. 2021Disparities in Dermatology AI: Assessments Using Diverse Clinical Imagescited by 132
  7. 2023SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysiscited by 44
  8. 2022Improving dermatology classifiers across populations using images generated by large diffusion modelscited by 25
  9. 2023Towards Reliable Dermatology Evaluation Benchmarkscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.