AIMC Topic: Neural Networks, Computer

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A Deep Neural Network Model using Random Forest to Extract Feature Representation for Gene Expression Data Classification.

Scientific reports
In predictive model development, gene expression data is associated with the unique challenge that the number of samples (n) is much smaller than the amount of features (p). This "n ≪ p" property has prevented classification of gene expression data f...

Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks.

Physics in medicine and biology
Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where detailed knowledge of the pathways of microscopic tumor...

Improved model adaptation approach for recognition of reduced-frame-rate continuous speech.

PloS one
In distributed speech recognition applications, the front-end device that stands for any handheld electronic device like smartphones and personal digital assistants (PDAs) captures the speech signal, extracts the speech features, and then sends the s...

Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces.

IEEE transactions on neural networks and learning systems
The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analys...

A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation.

IEEE journal of biomedical and health informatics
The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous mod...

Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning.

Radiology
Purpose To examine Generative Visual Rationales (GVRs) as a tool for visualizing neural network learning of chest radiograph features in congestive heart failure (CHF). Materials and Methods A total of 103 489 frontal chest radiographs in 46 712 pati...

Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.

Eye (London, England)
Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very ...

Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features.

Scientific reports
Long non-coding RNAs are involved in biological processes throughout the cell including the nucleus, chromatin and cytosol. However, most lncRNAs remain unannotated and functional annotation of lncRNAs is difficult due to their low conservation and t...