TY - GEN
T1 - Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset
AU - Groh, Matthew
AU - Harris, Caleb
AU - Soenksen, Luis
AU - Lau, Felix
AU - Han, Rachel
AU - Kim, Aerin
AU - Koochek, Arash
AU - Badri, Omar
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision models can serve as a useful decision support tool in healthcare and provide dermatologist-level classification on a number of specific tasks, darker skin is under-represented in the data. Most publicly available data sets do not include Fitzpatrick skin type labels. We annotate 16, 577 clinical images sourced from two dermatology atlases with Fitzpatrick skin type labels and open-source these annotations. Based on these labels, we find that there are significantly more images of light skin types than dark skin types in this dataset. We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on. In addition, we evaluate how an algorithmic approach to identifying skin tones, individual typology angle, compares with Fitzpatrick skin type labels annotated by a team of human labelers.
AB - How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision models can serve as a useful decision support tool in healthcare and provide dermatologist-level classification on a number of specific tasks, darker skin is under-represented in the data. Most publicly available data sets do not include Fitzpatrick skin type labels. We annotate 16, 577 clinical images sourced from two dermatology atlases with Fitzpatrick skin type labels and open-source these annotations. Based on these labels, we find that there are significantly more images of light skin types than dark skin types in this dataset. We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on. In addition, we evaluate how an algorithmic approach to identifying skin tones, individual typology angle, compares with Fitzpatrick skin type labels annotated by a team of human labelers.
UR - http://www.scopus.com/inward/record.url?scp=85115835039&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115835039&partnerID=8YFLogxK
U2 - 10.1109/CVPRW53098.2021.00201
DO - 10.1109/CVPRW53098.2021.00201
M3 - Conference contribution
AN - SCOPUS:85115835039
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1820
EP - 1828
BT - Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PB - IEEE Computer Society
T2 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Y2 - 19 June 2021 through 25 June 2021
ER -