Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fid...
Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor ...
Predictive maintenance is increasingly advancing into the aerospace industry, and it comes with diverse prognostic health management solutions. This type of maintenance can unlock several benefits for aerospace organizations. Such as preventing unexp...
There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machin...
IEEE journal of biomedical and health informatics
May 11, 2020
Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introdu...
PURPOSE: An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy because Bl...
This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in...
Medical & biological engineering & computing
May 11, 2020
Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectru...
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event...
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding a...
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