MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data
This addresses the need for trustworthy and responsible AI in high-stake health decisions by providing interpretability in multimodal fusion, though it appears incremental as it combines existing techniques like deep learning and GP-GOMEA.
The authors tackled the problem of building interpretable prediction models from multimodal data, particularly in health domains, by proposing MultiFIX, a pipeline that induces separate features from different data types and uses symbolic expressions for fusion, achieving results demonstrated on synthetic problems and a skin lesion detection dataset.
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.