  The initial assessment of skin lesions is typically based on dermoscopic
images. As this is a difficult and time-consuming task, machine learning
methods using dermoscopic images have been proposed to assist human experts.
Other approaches have studied electrical impedance spectroscopy (EIS) as a
basis for clinical decision support systems. Both methods represent different
ways of measuring skin lesion properties as dermoscopy relies on visible light
and EIS uses electric currents. Thus, the two methods might carry complementary
features for lesion classification. Therefore, we propose joint deep learning
models considering both EIS and dermoscopy for melanoma detection. For this
purpose, we first study machine learning methods for EIS that incorporate
domain knowledge and previously used heuristics into the design process. As a
result, we propose a recurrent model with state-max-pooling which automatically
learns the relevance of different EIS measurements. Second, we combine this new
model with different convolutional neural networks that process dermoscopic
images. We study ensembling approaches and also propose a cross-attention
module guiding information exchange between the EIS and dermoscopy model. In
general, combinations of EIS and dermoscopy clearly outperform models that only
use either EIS or dermoscopy. We show that our attention-based, combined model
outperforms other models with specificities of 34.4% (CI 31.3-38.4), 34.7% (CI
31.0-38.8) and 53.7% (CI 50.1-57.6) for dermoscopy, EIS and the combined model,
respectively, at a clinically relevant sensitivity of 98%.
