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المؤلفHamdi, Ali
المؤلفAboeleneen, Amr
المؤلفShaban, Khaled
تاريخ الإتاحة2022-12-21T10:01:47Z
تاريخ النشر2021
اسم المنشورLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/978-3-030-87156-7_2
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37515
الملخصExisting 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.
اللغةen
الناشرSpringer Science and Business Media Deutschland GmbH
الموضوعDeep architecture
Lung disease prediction
Multimodal representation learning
Visual uncertainty
العنوانMARL: Multimodal Attentional Representation Learning for Disease Prediction
النوعConference Paper
الصفحات14-27
رقم المجلد12899 LNCS
dc.accessType Abstract Only


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