AIMC Topic: Algorithms

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Deep learning conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study.

World journal of gastroenterology
BACKGROUND: Traditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional app...

Regularized Bayesian transfer learning for population-level etiological distributions.

Biostatistics (Oxford, England)
Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific ...

Replay in Deep Learning: Current Approaches and Missing Biological Elements.

Neural computation
Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a cr...

Dynamic Spatiotemporal Pattern Recognition With Recurrent Spiking Neural Network.

Neural computation
Our real-time actions in everyday life reflect a range of spatiotemporal dynamic brain activity patterns, the consequence of neuronal computation with spikes in the brain. Most existing models with spiking neurons aim at solving static pattern recogn...

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

PULMONARY NODULE DETECTION IN CHEST CT USING A DEEP LEARNING-BASED RECONSTRUCTION ALGORITHM.

Radiation protection dosimetry
This study's aim was to assess whether deep learning image reconstruction (DLIR) techniques are non-inferior to ASIR-V for the clinical task of pulmonary nodule detection in chest computed tomography. Up to 6 (range 3-6, mean 4.2) artificial lung nod...

Accurate segmentation for different types of lung nodules on CT images using improved U-Net convolutional network.

Medicine
Since lung nodules on computed tomography images can have different shapes, contours, textures or locations and may be attached to neighboring blood vessels or pleural surfaces, accurate segmentation is still challenging. In this study, we propose an...

In-line monitoring and endpoint determination of percolation process of herbal medicine using ultraviolet spectroscopy combined with convolutional neural network.

The Journal of pharmacy and pharmacology
OBJECTIVES: As a common step in the herbal medicine production process, percolation usually lacks effective process monitoring methods and is often conducted with fixed process parameters. In this study, an in-line ultraviolet (UV) spectroscopy was u...

ConvNets for counting: Object detection of transient phenomena in steelpan drums.

The Journal of the Acoustical Society of America
We train an object detector built from convolutional neural networks to count interference fringes in elliptical antinode regions in frames of high-speed video recordings of transient oscillations in Caribbean steelpan drums, illuminated by electroni...