MARL: Multimodal Attentional Representation Learning for Disease Prediction

TL;DR

MARL is a multimodal attentional learning model that integrates CT images and patient records to improve lung disease prediction, outperforming existing models in accuracy and regression metrics.

Contribution

The paper introduces MARL, a novel architecture combining fuzzy segmentation, CNN, LSTM, and attention mechanisms for multimodal disease prediction under uncertainty.

Findings

MARL achieves 91% R^2 in disease progression regression.

MARL attains 97% accuracy in binary classification.

MARL outperforms state-of-the-art CNN models by 19-57% in accuracy.

Abstract

Existing learning models often utilise CT-scan images to predict lung diseases. These models are posed by high uncertainties that affect lung segmentation and visual feature learning. We introduce MARL, a novel Multimodal Attentional Representation Learning model architecture that learns useful features from multimodal data under uncertainty. We feed the proposed model with both the lung CT-scan images and their perspective historical patients' biological records collected over times. Such rich data offers to analyse both spatial and temporal aspects of the disease. MARL employs Fuzzy-based image spatial segmentation to overcome uncertainties in CT-scan images. We then utilise a pre-trained Convolutional Neural Network (CNN) to learn visual representation vectors from images. We augment patients' data with statistical features from the segmented images. We develop a Long Short-Term Memory (LSTM) network to represent the augmented data and learn sequential patterns of disease progressions. Finally, we inject both CNN and LSTM feature vectors to an attention layer to help focus on the best learning features. We evaluated MARL on regression of lung disease progression and status classification. MARL outperforms state-of-the-art CNN architectures, such as EfficientNet and DenseNet, and baseline prediction models. It achieves a 91% R^2 score, which is higher than the other models by a range of 8% to 27%. Also, MARL achieves 97% and 92% accuracy for binary and multi-class classification, respectively. MARL improves the accuracy of state-of-the-art CNN models with a range of 19% to 57%. The results show that combining spatial and sequential temporal features produces better discriminative feature.