AIMC Topic: Neural Networks, Computer

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Training deep neural density estimators to identify mechanistic models of neural dynamics.

eLife
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challen...

Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan.

BMC bioinformatics
BACKGROUND: Introducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research. But mostly, it has been focusing on the performance of the prediction modeling for disease-related...

A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network.

BMC bioinformatics
BACKGROUND: Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new...

Biomedical document triage using a hierarchical attention-based capsule network.

BMC bioinformatics
BACKGROUND: Biomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. I...

Turing Universality of Weighted Spiking Neural P Systems with Anti-spikes.

Computational intelligence and neuroscience
Weighted spiking neural P systems with anti-spikes (AWSN P systems) are proposed by adding anti-spikes to spiking neural P systems with weighted synapses. Anti-spikes behave like spikes of inhibition of communication between neurons. Both spikes and ...

On stability and associative recall of memories in attractor neural networks.

PloS one
Attractor neural networks such as the Hopfield model can be used to model associative memory. An efficient associative memory should be able to store a large number of patterns which must all be stable. We study in detail the meaning and definition o...

Diagnosis of pharyngeal cancer on endoscopic video images by Mask region-based convolutional neural network.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: We aimed to develop an artificial intelligence (AI) system for the real-time diagnosis of pharyngeal cancers.

Deep learning method for segmentation of rotator cuff muscles on MR images.

Skeletal radiology
OBJECTIVE: To develop and validate a deep convolutional neural network (CNN) method capable of (1) selecting a specific shoulder sagittal MR image (Y-view) and (2) automatically segmenting rotator cuff (RC) muscles on a Y-view. We hypothesized a CNN ...

Quaternion Spiking and Quaternion Quantum Neural Networks: Theory and Applications.

International journal of neural systems
Biological evidence shows that there are neural networks specialized for recognition of signals and patterns acting as associative memories. The spiking neural networks are another kind which receive input from a broad range of other brain areas to p...

DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.

Proteins
Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network...