Physical and engineering sciences in medicine
Oct 22, 2020
Clinical depression is a neurological disorder that can be identified by analyzing the Electroencephalography (EEG) signals. However, the major drawback in using EEG to accurately identify depression is the complexity and variation that exist in the ...
Deep neural networks often achieve high predictive accuracy on biological problems, but it can be hard to contextualize how and explain why predictions are made. In this issue, Kuenzi et al. model the sensitivity of cancers to drugs using deep neural...
With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can di...
To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast densi...
Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used...
Computational intelligence and neuroscience
Oct 22, 2020
Context-aware citation recommendation aims to automatically predict suitable citations for a given citation context, which is essentially helpful for researchers when writing scientific papers. In existing neural network-based approaches, overcorrela...
Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and...
BACKGROUND: Inguinal hernia repair is one of the most commonly performed surgical procedures. We developed and validated an artificial neural network (ANN) model for the prediction of surgical outcomes and the analysis of risk factors for inguinal he...
In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brai...
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