Self-Attention Equipped Graph Convolutions for Disease Prediction
This work addresses disease prediction for medical applications by improving accuracy and efficiency, though it appears incremental as it builds on existing graph convolution techniques.
The authors tackled disease prediction using multi-modal data by proposing a graph convolution model with a novel self-attention layer to weight demographic elements, resulting in superior computational speed and performance compared to state-of-the-art methods.
Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such diverse data gives complementary information about the patientś condition to make an informed diagnosis. A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction. We propose a graph convolution based deep model which takes into account the distinctiveness of each element of the multi-modal data. We incorporate a novel self-attention layer, which weights every element of the demographic data by exploring its relation to the underlying disease. We demonstrate the superiority of our developed technique in terms of computational speed and performance when compared to state-of-the-art methods. Our method outperforms other methods with a significant margin.