AIMC Topic: Deep Learning

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Activity-aware essential tremor evaluation using deep learning method based on acceleration data.

Parkinsonism & related disorders
BACKGROUND: Essential tremor (ET), one of the most common neurological disorders is typically evaluated with validated rating scales which only provide a subjective assessment during a clinical visit, underestimating the fluctuations tremor during di...

Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor.

Sensors (Basel, Switzerland)
Iris recognition systems have been used in high-security-level applications because of their high recognition rate and the distinctiveness of iris patterns. However, as reported by recent studies, an iris recognition system can be fooled by the use o...

Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective.

IEEE reviews in biomedical engineering
With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical tr...

End-to-End Representation Learning for Chemical-Chemical Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a ...

Deep learning to predict the lab-of-origin of engineered DNA.

Nature communications
Genetic engineering projects are rapidly growing in scale and complexity, driven by new tools to design and construct DNA. There is increasing concern that widened access to these technologies could lead to attempts to construct cells for malicious i...

Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes.

BMC bioinformatics
BACKGROUND: Nuclear segmentation is an important step for profiling aberrant regions of histology sections. If nuclear segmentation can be resolved, then new biomarkers of nuclear phenotypes and their organization can be predicted for the application...

Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images.

International journal of computer assisted radiology and surgery
PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) is a subcellular in vivo imaging technique capable of producing images that enable diagnosis of malign structural modifications in epithelial tissue. Images acquired with pCLE are, however, of...

TractSeg - Fast and accurate white matter tract segmentation.

NeuroImage
The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection ...

Building medical image classifiers with very limited data using segmentation networks.

Medical image analysis
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre-trained classification models can alleviate the problem,...

Predict effective drug combination by deep belief network and ontology fingerprints.

Journal of biomedical informatics
The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief netwo...