AIMC Topic: Machine Learning

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Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning.

Scientific reports
In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which...

A machine learning approach for predicting methionine oxidation sites.

BMC bioinformatics
BACKGROUND: The oxidation of protein-bound methionine to form methionine sulfoxide, has traditionally been regarded as an oxidative damage. However, recent evidences support the view of this reversible reaction as a regulatory post-translational modi...

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

PLoS computational biology
Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of s...

Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes.

NeuroImage. Clinical
OBJECTIVE: To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity.

A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints.

Ultrasound in medicine & biology
During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be di...

A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time.

Computers in biology and medicine
This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algor...

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

Computers in biology and medicine
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epi...

Deep learning guided stroke management: a review of clinical applications.

Journal of neurointerventional surgery
Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess...

Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database.

IEEE transactions on medical imaging
The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT ...