We've included the original image sources, the training script train.py, and a notebook to compare Fitzpatrick annotations with individual typology angle scores. You can read the dataset and our analysis in our paper.

We thank Scale AI for providing Fitzpatrick annotations for all images in this dataset pro bono.

You can find the Fitzpatrick annotations in fitzpatrick17k.csv. You can download the images from their original source, which is shared in the url column of the Fitzpatrick annotations .csv. Alternatively, fill out this form and contact us and we can provide a link to all the images.

The results from our paper can be replicated using train.py and ita_fitzpatrick_analysis.ipynb.

After you download the dataset, edit train.py by specifying the image directory of the dataset, and then run python train.py 20 full where 20 refers to the number of epochs and full refers to the full dataset.

You can check out our comparison of Fitzpatrick annotations and individual typology angle scores with the ita_fitzpatrick_analysis.ipynb

@article{groh2021evaluating,
  title={Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset},
  author={Groh, Matthew and Harris, Caleb and Soenksen, Luis and Lau, Felix and Han, Rachel and Kim, Aerin and Koochek, Arash and Badri, Omar},
  journal={arXiv preprint arXiv:2104.09957},
  year={2021}
}

Original images collected from Atlas Dermatologico and DermaAmin

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.