AIMC Topic: Reproducibility of Results

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Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.

NeuroImage. Clinical
Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group...

Digging deep into Golgi phenotypic diversity with unsupervised machine learning.

Molecular biology of the cell
The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportuni...

Simultaneous LC-MS/MS determination of 40 legal and illegal psychoactive drugs in breast and bovine milk.

Food chemistry
This work presents a fast, sensitive and reliable multi-residue methodology based on fat and protein precipitation and liquid chromatography-tandem mass spectrometry for the determination of common legal and illegal psychoactive drugs, and major meta...

A robotic C-arm cone beam CT system for image-guided proton therapy: design and performance.

The British journal of radiology
OBJECTIVE: A ceiling-mounted robotic C-arm cone beam CT (CBCT) system was developed for use with a 190° proton gantry system and a 6-degree-of-freedom robotic patient positioner. We report on the mechanical design, system accuracy, image quality, ima...

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection.

Sensors (Basel, Switzerland)
For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning comb...

Learning ensemble classifiers for diabetic retinopathy assessment.

Artificial intelligence in medicine
Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doct...

Compensation for Magnetic Disturbances in Motion Estimation to Provide Feedback to Wearable Robotic Systems.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The direction of the Earth's magnetic field is used as a reference vector to determine the heading in orientation estimation with wearable sensors. However, the magnetic field strength is weak and can be easily disturbed in the vicinity of ferromagne...

Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill ...

Diabetic retinopathy screening using deep neural network.

Clinical & experimental ophthalmology
IMPORTANCE: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening.

Using machine learning algorithms to identify genes essential for cell survival.

BMC bioinformatics
BACKGROUND: With the explosion of data comes a proportional opportunity to identify novel knowledge with the potential for application in targeted therapies. In spite of this huge amounts of data, the solutions to treating complex disease is elusive....