AIMC Topic: Neural Networks, Computer

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RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.

Scientific reports
Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses are closely associated with genomic alterations in cancer cells. Numerous state-of-the-art machine le...

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising.

Physiological measurement
OBJECTIVE: Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. However, the low signal-to-noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of t...

Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network.

Physics in medicine and biology
A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm, is presented. 3D radiofrequency ultrasound signals were collected from 13 patients using the linear...

External validation and transfer learning of convolutional neural networks for computed tomography dental artifact classification.

Physics in medicine and biology
Quality assurance of data prior to use in automated pipelines and image analysis would assist in safeguarding against biases and incorrect interpretation of results. Automation of quality assurance steps would further improve robustness and efficienc...

Towards a global understanding of the drivers of marine and terrestrial biodiversity.

PloS one
Understanding the distribution of life's variety has driven naturalists and scientists for centuries, yet this has been constrained both by the available data and the models needed for their analysis. Here we compiled data for over 67,000 marine and ...

The impact of different training load quantification and modelling methodologies on performance predictions in elite swimmers.

European journal of sport science
The use of rolling averages to analyse training data has been debated recently. We evaluated two training load quantification methods (five-zone, seven-zone) fitted to performances over two race distances (50 and 100 m) using four separate longitudin...

SDCT-AuxNet: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis.

Medical image analysis
Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are...

Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks.

Medical image analysis
Thyroid cancer is a disease in which the first symptom is a nodule in the thyroid region of the neck. It is one of the cancers with the highest incidences, and has the highest increase rate in the last thirty years. Ultrasonography is one of the most...

A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature.

Journal of biomedical informatics
Recently joint modeling methods of entity and relation exhibit more promising results than traditional pipelined methods in general domain. However, they are inappropriate for the biomedical domain due to numerous overlapping relations in biomedical ...