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Image manipulation with natural language using Two-sided Attentive Conditional Generative Adversarial Network.

Neural networks : the official journal of the International Neural Network Society
Altering the content of an image with photo editing tools is a tedious task for an inexperienced user, especially, when modifying the visual attributes of a specific object in an image without affecting other constituents such as background etc. To s...

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

Artificial intelligence in medicine
Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detec...

fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.

NeuroImage
Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area...

Multi-dimensional predictions of psychotic symptoms via machine learning.

Human brain mapping
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have pr...

MGAT: Multi-view Graph Attention Networks.

Neural networks : the official journal of the International Neural Network Society
Multi-view graph embedding is aimed at learning low-dimensional representations of nodes that capture various relationships in a multi-view network, where each view represents a type of relationship among nodes. Multitudes of existing graph embedding...

Pain intensity estimation based on a spatial transformation and attention CNN.

PloS one
Models designed to detect abnormalities that reflect disease from facial structures are an emerging area of research for automated facial analysis, which has important potential value in smart healthcare applications. However, most of the proposed mo...

Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding.

Journal of neural engineering
OBJECTIVE: A hearing aid's noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-st...

High tissue contrast image synthesis via multistage attention-GAN: Application to segmenting brain MR scans.

Neural networks : the official journal of the International Neural Network Society
Magnetic resonance imaging (MRI) presents a detailed image of the internal organs via a magnetic field. Given MRI's non-invasive advantage in repeated imaging, the low-contrast MR images in the target area make segmentation of tissue a challenging pr...

Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images.

Ultrasound in medicine & biology
Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach t...

Automated diagnosis of bone metastasis based on multi-view bone scans using attention-augmented deep neural networks.

Medical image analysis
Bone scintigraphy is accepted as an effective diagnostic tool for whole-body examination of bone metastasis. However, the manual analysis of bone scintigraphy images requires extensive experience and is exhausting and time-consuming. An automated dia...