AIMC Topic: Neural Networks, Computer

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Introduction to artificial intelligence in medicine.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
The term Artificial Intelligence (AI) was coined by John McCarthy in 1956 during a conference held on this subject. However, the possibility of machines being able to simulate human behavior and actually think was raised earlier by Alan Turing who de...

Rice Blast Disease Recognition Using a Deep Convolutional Neural Network.

Scientific reports
Rice disease recognition is crucial in automated rice disease diagnosis systems. At present, deep convolutional neural network (CNN) is generally considered the state-of-the-art solution in image recognition. In this paper, we propose a novel rice bl...

Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks.

BMC genomics
BACKGROUND: Identifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are de...

A trial for EBT3 film without batch-specific calibration using a neural network.

Physics in medicine and biology
This note reports a trial to establish an ANN (artificial neural network) method applying to EBT3 films of different batches without batch-specific calibration. Based on Pytorch (Facebook, https://pytorch.org/), a feed-forward ANN model was built to ...

Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.

PloS one
Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is key to full patient recovery. The Microscopic Observed Drug Susceptibility (MODS) is a test to diagnos...

Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks.

IEEE transactions on medical imaging
Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may...

Deep Attention Network for Egocentric Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recognizing a camera wearer's actions from videos captured by an egocentric camera is a challenging task. In this paper, we employ a two-stream deep neural network composed of an appearance-based stream and a motion-based stream to recognize egocentr...

Attention Inspired Network: Steep learning curve in an invariant pattern recognition model.

Neural networks : the official journal of the International Neural Network Society
Hubel and Wiesel's study about low areas of the visual cortex (VC) inspired deep models for invariant pattern recognition. In such models, simple and complex layers alternate local feature extraction with subsampling to add invariance to distortion o...

Deep learning for electroencephalogram (EEG) classification tasks: a review.

Journal of neural engineering
OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools ...

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

Molecular pharmaceutics
The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reverse transcr...