AIMC Topic: Neural Networks, Computer

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Population pharmacokinetic model selection assisted by machine learning.

Journal of pharmacokinetics and pharmacodynamics
A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine ...

White matter hyperintensities segmentation using an ensemble of neural networks.

Human brain mapping
White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network ...

Deep Learning Image Analysis of High-Throughput Toxicology Assay Images.

SLAS discovery : advancing life sciences R & D
High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-con...

Hierarchical graph representations in digital pathology.

Medical image analysis
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological ent...

AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algori...

Current applications of artificial intelligence in vascular surgery.

Seminars in vascular surgery
Basic foundations of artificial intelligence (AI) include analyzing large amounts of data, recognizing patterns, and predicting outcomes. At the core of AI are well-defined areas, such as machine learning, natural language processing, artificial neur...

Explainable deep learning ensemble for food image analysis on edge devices.

Computers in biology and medicine
Food recognition systems recently garnered much research attention in the relevant field due to their ability to obtain objective measurements for dietary intake. This feature contributes to the management of various chronic conditions. Challenges su...

A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.

Cell reports methods
MOTIVATION: Quantitative studies of cellular morphodynamics rely on extracting leading-edge velocity time series based on accurate cell segmentation from live cell imaging. However, live cell imaging has numerous challenging issues regarding accurate...

Artificial Neural Networks as Propagators in Quantum Dynamics.

The journal of physical chemistry letters
The utilization of artificial neural networks (ANNs) provides strategies for accelerating molecular simulations. Herein, ANNs are implemented as propagators of the time-dependent Schrödinger equation to simulate the quantum dynamics of systems with t...

AANet: Adaptive Attention Network for COVID-19 Detection From Chest X-Ray Images.

IEEE transactions on neural networks and learning systems
Accurate and rapid diagnosis of COVID-19 using chest X-ray (CXR) plays an important role in large-scale screening and epidemic prevention. Unfortunately, identifying COVID-19 from the CXR images is challenging as its radiographic features have a vari...