  While deep learning-based computer-aided diagnosis for skin lesion image
analysis is approaching dermatologists' performance levels, there are several
works showing that incorporating additional features such as shape priors,
texture, color constancy, and illumination further improves the lesion
diagnosis performance. In this work, we look at another clinically useful
feature, skin lesion elevation, and investigate the feasibility of predicting
and leveraging skin lesion elevation labels. Specifically, we use a deep
learning model to predict image-level lesion elevation labels from 2D skin
lesion images. We test the elevation prediction accuracy on the derm7pt
dataset, and use the elevation prediction model to estimate elevation labels
for images from five other datasets: ISIC 2016, 2017, and 2018 Challenge
datasets, MSK, and DermoFit. We evaluate cross-domain generalization by using
these estimated elevation labels as auxiliary inputs to diagnosis models, and
show that these improve the classification performance, with AUROC improvements
of up to 6.29% and 2.69% for dermoscopic and clinical images, respectively. The
code is publicly available at https://github.com/sfu-mial/LesionElevation.
