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A lot of deep learning (DL) research these days is mainly focused onimproving quantitative metrics regardless of other factors. In human-centeredapplications, like skin lesion classification in dermatology, DL-drivenclinical decision support systems are still in their infancy due to the limitedtransparency of their decision-making process. Moreover, the lack of proceduresthat can explain the behavior of trained DL algorithms leads to almost no trustfrom clinical physicians. To diagnose skin lesions, dermatologists rely onvisual assessment of the disease and the data gathered from the patient'sanamnesis. Data-driven algorithms dealing with multi-modal data are limited bythe separation of feature-level and decision-level fusion procedures requiredby convolutional architectures. To address this issue, we enable single-stagemulti-modal data fusion via the attention mechanism of transformer-basedarchitectures to aid in diagnosing skin diseases. Our method beats otherstate-of-the-art single- and multi-modal DL architectures in image-rich andpatient-data-rich environments. Additionally, the choice of the architectureenables native interpretability support for the classification task both in theimage and metadata domain with no additional modifications necessary.

Transformer-based interpretable multi-modal data fusion for skin lesion classification