AIMC Topic: Databases, Factual

Clear Filters Showing 2071 to 2080 of 3080 articles

Benchmarking deep learning models on large healthcare datasets.

Journal of biomedical informatics
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist w...

A novel application of deep learning for single-lead ECG classification.

Computers in biology and medicine
Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram ...

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.

Medical image analysis
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
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
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
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
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
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 ...

A Fungus Spores Dataset and a Convolutional Neural Network Based Approach for Fungus Detection.

IEEE transactions on nanobioscience
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...