Abstract
Skin diseases, such as skin cancer, are a significant public
health issue, and early diagnosis is crucial for effective treatment. Artificial
intelligence (AI) algorithms have the potential to assist in triaging benign
vs malignant skin lesions and improve diagnostic accuracy. However, ex-
isting AI models for skin disease diagnosis are often developed and tested
on limited and biased datasets, leading to poor performance on certain
skin tones. To address this problem, we propose a novel generative model,
named DermDiff, that can generate diverse and representative dermo-
scopic image data for skin disease diagnosis. Leveraging text prompting
and multimodal image-text learning, DermDiff improves the represen-
tation of underrepresented groups (patients, diseases, etc.) in highly
imbalanced datasets. Our extensive experimentation showcases the effec-
tiveness of DermDiff in terms of high fidelity and diversity. Furthermore,
downstream evaluation suggests the potential of DermDiff in mitigating
racial biases for dermatology diagnosis.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/ADSMI2024_170.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{MunNus_DermDiff_MICCAISAT2026,
author = { Munia, Nusrat AND Imran, Abdullah-Al-Zubaer},
title = { { DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17276},
month = {pending},
page = {pending}
}