AIMC Topic: Neural Networks, Computer

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Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network.

Radiological physics and technology
The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through...

Fine-Grained Face Annotation Using Deep Multi-Task CNN.

Sensors (Basel, Switzerland)
We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct...

Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET).

Environmental monitoring and assessment
Over the last few years, the uses of artificial intelligence techniques (AI) for modeling daily reference evapotranspiration (ET) have become more popular and a considerable amount of models were successfully applied to the problem. Therefore, in the...

Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.

The British journal of radiology
OBJECTIVE:: Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sensitive mutations of the epidermal growth factor receptor (EGFR) gene. We constructed three-dimension...

Neural circuits for learning context-dependent associations of stimuli.

Neural networks : the official journal of the International Neural Network Society
The use of reinforcement learning combined with neural networks provides a powerful framework for solving certain tasks in engineering and cognitive science. Previous research shows that neural networks have the power to automatically extract feature...

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Journal of proteome research
Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to ...

Automated deep-neural-network surveillance of cranial images for acute neurologic events.

Nature medicine
Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'. Although these disorders are often recognizabl...

DeepSort: deep convolutional networks for sorting haploid maize seeds.

BMC bioinformatics
BACKGROUND: Maize is a leading crop in the modern agricultural industry that accounts for more than 40% grain production worldwide. THe double haploid technique that uses fewer breeding generations for generating a maize line has accelerated the pace...

Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms.

BMC bioinformatics
BACKGROUND: The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more acc...