Equi-Derm: Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation [arxiv]

• Accepted in MI4EGH (AAAI Fall Symposium 2024)

Abstract: In the realm of dermatology, the complexity of diagnosing skin conditions manually requires the expertise of dermatologists. Accurate identification of various skin ailments, ranging from cancer to inflammatory diseases, is paramount. However, existing artificial intelligence (AI) models in dermatology face challenges, particularly in accurately diagnosing diseases across diverse skin tones, with a notable performance gap in darker skin. Furthermore, the scarcity of publicly available and unbiased data sets hampers the development of inclusive AI diagnostic tools. To address the challenges in accurately predicting skin conditions across diverse skin tones, we employ a transfer-learning approach that capitalizes on the rich and transferable knowledge from various image domains. Our method integrates multiple pre-trained models from a wide range of sources, including general and specific medical images, to improve the robustness and inclusiveness of the skin condition predictions. We rigorously evaluated the effectiveness of these models using the Diverse Dermatology Images (DDI) dataset, which uniquely encompasses both underrepresented and common skin tones, making it an ideal benchmark for assessing our approach. Among all methods, Med-ViT emerged as the top performer due to its comprehensive feature representation learned from diverse image sources. To further enhance performance, we performed domain adaptation using additional skin image datasets such as HAM10000. This adaptation significantly improved model performance across all models.

Data Download

• Download DDI data from here
• Follow our train-val-test split as described in the paper and create thress sub-directories train, val, test and put images in each label directory inside those sub-directories.
• The file hiererchy should look like this:
  • train
    • benign
    • malignant
  • val
    • benign
    • malignant
  • test
    • benign
    • malignant

Download Pre-trained weights

• Download corresponding model weights from the method papers before running the finetuning scripts

For Citation

  title={Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation},
  author={Dip, Sajib Acharjee and Arif, Kazi Hasan Ibn and Shuvo, Uddip Acharjee and Khan, Ishtiaque Ahmed and Meng, Na},
  journal={arXiv preprint arXiv:2409.00873},
  year={2024}
}