AIMC Topic: Neural Networks, Computer

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Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes.

SLAS discovery : advancing life sciences R & D
The quantification and identification of cellular phenotypes from high-content microscopy images has proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use cla...

Artificial neural networks in the selection of shoe lasts for people with mild diabetes.

Medical engineering & physics
This research addressed the selection of shoe lasts for footwear design to help relieve the pain associated with diabetic neuropathy and foot ulcers. A reverse engineering (RE) technique was used to convert point clouds corresponding to scanned shoe ...

Task representations in neural networks trained to perform many cognitive tasks.

Nature neuroscience
The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cogniti...

Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation.

Journal of healthcare engineering
Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolut...

Application of convolutional neural networks for classification of adult mosquitoes in the field.

PloS one
Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of ...

Navigating Virtual Environments Using Leg Poses and Smartphone Sensors.

Sensors (Basel, Switzerland)
Realization of navigation in virtual environments remains a challenge as it involves complex operating conditions. Decomposition of such complexity is attainable by fusion of sensors and machine learning techniques. Identifying the right combination ...

Semi-supervised encoding for outlier detection in clinical observation data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Electronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible obser...

Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling.

Medical image analysis
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multip...

Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.

Behavioural brain research
BACKGROUND: Previous automated EEG-based diagnosis of autism spectrum disorders (ASD) using various nonlinear EEG analysis methods were limited to distinguish only children with ASD from those normally developed without approaching their autistic fea...

Evaluation of data-driven models for predicting the service life of concrete sewer pipes subjected to corrosion.

Journal of environmental management
Concrete corrosion is one of the most significant failure mechanisms of sewer pipes, and can reduce the sewer service life significantly. To facilitate the management and maintenance of sewers, it is essential to obtain reliable prediction of the exp...