Mafalda Malafaia

Mafalda Malafaia, Peter A. N. Bosman, Coen Rasch et al.

Accurate and interpretable survival analysis remains a core challenge in oncology. With growing multimodal data and the clinical need for transparent models to support validation and trust, this challenge increases in complexity. We propose an interpretable multimodal AI framework to automate survival analysis by integrating clinical variables and computed tomography imaging. Our MultiFIX-based framework uses deep learning to infer survival-relevant features that are further explained: imaging features are interpreted via Grad-CAM, while clinical variables are modeled as symbolic expressions through genetic programming. Risk estimation employs a transparent Cox regression, enabling stratification into groups with distinct survival outcomes. Using the open-source RADCURE dataset for head and neck cancer, MultiFIX achieves a C-index of 0.838 (prediction) and 0.826 (stratification), outperforming the clinical and academic baseline approaches and aligning with known prognostic markers. These results highlight the promise of interpretable multimodal AI for precision oncology with MultiFIX.

Mafalda Malafaia, Thalea Schlender, Peter A. N. Bosman et al.

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.