AIMC Topic:
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Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.
Medical image analysis
Jun 2, 2018
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patien...
Ischemic stroke lesion segmentation using stacked sparse autoencoder.
Computers in biology and medicine
Jun 2, 2018
Automatic segmentation of ischemic stroke lesion volumes from multi-spectral Magnetic Resonance Imaging (MRI) sequences plays a vital role in quantifying and locating the lesion region. Most existing methods mainly rely on designing hand-crafted feat...
Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition.
IEEE transactions on pattern analysis and machine intelligence
Jun 1, 2018
Heterogeneous face recognition (HFR) aims at matching facial images acquired from different sensing modalities with mission-critical applications in forensics, security and commercial sectors. However, HFR presents more challenging issues than tradit...
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training.
IEEE transactions on medical imaging
Jun 1, 2018
To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire due to privacy issues, lack of experts available for annotation, underrepresentation of rar...
Pattern Classification for Gastrointestinal Stromal Tumors by Integration of Radiomics and Deep Convolutional Features.
IEEE journal of biomedical and health informatics
May 29, 2018
Predicting malignant potential is one of the most critical components of a computer-aided diagnosis system for gastrointestinal stromal tumors (GISTs). These tumors have been studied only on the basis of subjective computed tomography findings. Among...
Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme.
Computer methods and programs in biomedicine
May 29, 2018
BACKGROUND: In the time since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in the formulation of an effective and efficient strategy to improve the participation rate has been growing. The aim ...
Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care.
Journal of clinical monitoring and computing
May 24, 2018
Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this stu...
False Positive Reactions
Brain Injuries, Traumatic
Brain Injuries
Area Under Curve
Sample Size
Bayes Theorem
Signal Processing, Computer-Assisted
Hypotension
Critical Care
Middle Aged
Diagnosis, Computer-Assisted
Pilot Projects
Adolescent
Adult
Sensitivity and Specificity
Software
Male
Databases, Factual
Female
Prospective Studies
Humans
Aged
Neural Networks, Computer
Intensive Care Units
Aged, 80 and over
Machine Learning
Young Adult
A Fungus Spores Dataset and a Convolutional Neural Network Based Approach for Fungus Detection.
IEEE transactions on nanobioscience
May 22, 2018
Fungus is enormously notorious for food, human health, and archives. Fungus sign and symptoms in medical science are non-specific and asymmetrical for extremely large areas resulting into a challenging task of fungal detection. Various traditional an...
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction.
IEEE/ACM transactions on computational biology and bioinformatics
May 21, 2018
Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the developm...
Tensor-driven extraction of developmental features from varying paediatric EEG datasets.
Journal of neural engineering
May 21, 2018
OBJECTIVE: Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usabil...
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