Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models

TL;DR

This study develops joint deep learning models that integrate electrical impedance spectroscopy and dermoscopy images to improve melanoma detection accuracy, demonstrating that combining both methods outperforms individual approaches.

Contribution

It introduces a novel recurrent EIS model with domain knowledge and a cross-attention mechanism for combining EIS and dermoscopy in melanoma classification.

Findings

Combined models outperform single-modality models.

Attention-based models achieve higher specificity at 98% sensitivity.

Proposed methods improve clinical decision support for melanoma detection.

Abstract

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%.