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In recent years, cutting-edge computational technologies are increasingly being applied in clinical settings in order to provide higher quality of healthcare. Furthermore, huge amount of biomedical data is continuously acquired and stored from ever accurate medical devices. Conversely, in the scientific research community, there is a growing necessity to develop more optimized methodologies that are able to process big biomedical data and extract discriminating parameters (known as features) for more effective, predictive diagnosis of specific pathologies. To this end, biologically computational intelligence techniques are widely being applied in this field. In particular, recent advances in so called Deep Learning (DL) techniques – including, amongst others, convolutional neural networks, stacked autoencoders, deep reinforcement learning, adversarial learning, transfer learning, meta-learning, end-to-end learning, life-long learning and (semi-/un-) supervised learning with weakly labelled data, graph neural networks etc – have emerged as promising technologies in the clinical and biomedical research domains.

The proposed Special Issue aims to solicit original contributions to demonstrate the potential of DL based methodologies and computational models in challenging the current clinical and healthcare frameworks. Concurrently, to exploit the growing availability of multiple heterogeneous medical Big data (such as neuroimages, electrophysiological time-series, multi-modal biomedical data, electronic health records, etc.), the Special Issue focuses on latest advances in innovative DL approaches to process multi-data sources and develop more accurate, secure and explainable solutions, with potential for deployment in a range of future clinical and healthcare applications.

TOPICS

The topics of interest include, but are not limited to:

• Deep neural networks in clinical and biomedical healthcare applications
• Generative adversarial networks for health data (neuroimages, EEG etc)
• Multi-modal techniques and ensemble architectures
• Automated feature engineering and interpretation of features extracted from biomedical data via deep neural networks
• Transfer learning, meta-learning, end-to-end and deep lifelong learning approaches for improved detection of neuropathologies
• Supervised, unsupervised and semi-supervised learning with (weakly labelled) biomedical data (including electronic health records)
• Deep reinforcement learning and graphical neural networks for electrophysiological signals and /or neuroimages (MRI, fMRI, etc.)
• Deep neural networks for cyber and adversarial attacks in healthcare applications
• New or improved nature-inspired optimization algorithms for DL architectures in biomedical applications
• New hypercomplex deep learning models for 3D and multi-modal signals
• Explainable and privacy-assuring deep learning models and architectures