  Skin diseases affect millions of people worldwide, across all ethnicities.
Increasing diagnosis accessibility requires fair and accurate segmentation and
classification of dermatology images. However, the scarcity of annotated
medical images, especially for rare diseases and underrepresented skin tones,
poses a challenge to the development of fair and accurate models. In this
study, we introduce a Fair, Efficient, and Diverse Diffusion-based framework
for skin lesion segmentation and malignancy classification. FEDD leverages
semantically meaningful feature embeddings learned through a denoising
diffusion probabilistic backbone and processes them via linear probes to
achieve state-of-the-art performance on Diverse Dermatology Images (DDI). We
achieve an improvement in intersection over union of 0.18, 0.13, 0.06, and 0.07
while using only 5%, 10%, 15%, and 20% labeled samples, respectively.
Additionally, FEDD trained on 10% of DDI demonstrates malignancy classification
accuracy of 81%, 14% higher compared to the state-of-the-art. We showcase high
efficiency in data-constrained scenarios while providing fair performance for
diverse skin tones and rare malignancy conditions. Our newly annotated DDI
segmentation masks and training code can be found on
https://github.com/hectorcarrion/fedd.
