FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification
This work addresses the challenge of improving diagnosis accessibility for skin diseases across diverse populations, though it appears incremental as it builds on existing diffusion methods.
The paper tackles the problem of fair and accurate segmentation and classification of dermatology images, especially for underrepresented skin tones and rare diseases, by introducing a diffusion-based framework that achieves state-of-the-art performance with improvements in intersection over union up to 0.18 and a 14% higher malignancy classification accuracy.
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.