  In the health domain, decisions are often based on different data modalities.
Thus, when creating prediction models, multimodal fusion approaches that can
extract and combine relevant features from different data modalities, can be
highly beneficial. Furthermore, it is important to understand how each modality
impacts the final prediction, especially in high-stake domains, so that these
models can be used in a trustworthy and responsible manner. We propose
MultiFIX: a new interpretability-focused multimodal data fusion pipeline that
explicitly induces separate features from different data types that can
subsequently be combined to make a final prediction. An end-to-end deep
learning architecture is used to train a predictive model and extract
representative features of each modality. Each part of the model is then
explained using explainable artificial intelligence techniques. Attention maps
are used to highlight important regions in image inputs. Inherently
interpretable symbolic expressions, learned with GP-GOMEA, are used to describe
the contribution of tabular inputs. The fusion of the extracted features to
predict the target label is also replaced by a symbolic expression, learned
with GP-GOMEA. Results on synthetic problems demonstrate the strengths and
limitations of MultiFIX. Lastly, we apply MultiFIX to a publicly available
dataset for the detection of malignant skin lesions.
