Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep…
Medicine & Health
IWANN Special Collection 2019: Deep Learning Models in Healthcare and Biomedicine
When it comes to recent computational support for medical diagnosis from images or sequences, it is more and more intensively resorted to deep learning architectures. The performance and insights that these models provide to the clinicians are valuable for grasping crucial and sometimes specific characteristics of the disease and patient. This Special Collection extends the papers on the topic of deep learning modelling in healthcare and biomedicine presented at the IWANN conference.
Image Credit: IWANN
Introduction
IWANN 2019 Research Papers
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Image creditFig 4. An obtained SOM map on training data. by Stoean et al., CC BY 4.0PLOS ONE A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis
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PLOS ONE Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions,…
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PLOS ONE Quantile graphs for EEG-based diagnosis of Alzheimer’s disease
Known as a degenerative and progressive dementia, Alzheimer’s disease (AD) affects about 25 million elderly people around the world. This…
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Image creditFig 4. Treemapping procedure. by López-García et al., CC BY 4.0PLOS ONE Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data
Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient’s state…