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AuthorHamdi, Ali
AuthorAboeleneen, Amr
AuthorShaban, Khaled
Available date2022-12-21T10:01:47Z
Publication Date2021
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
URIhttp://dx.doi.org/10.1007/978-3-030-87156-7_2
URIhttp://hdl.handle.net/10576/37515
AbstractExisting 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 % R2 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. 2021, Springer Nature Switzerland AG.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectDeep architecture
Lung disease prediction
Multimodal representation learning
Visual uncertainty
TitleMARL: Multimodal Attentional Representation Learning for Disease Prediction
TypeConference Paper
Pagination14-27
Volume Number12899 LNCS
dc.accessType Abstract Only


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