Staffing & Scheduling

Uncertainty-aware domain alignment for anatomical structure segmentation.

Automatic and accurate segmentation of anatomical structures on medical images is crucial for detecting various potential diseases. However, the segmentation performance of established deep neural networks may degenerate on different modalities or devices owing to the significant difference across the domains, a problem known as domain shift. In this work, we propose an uncertainty-aware domain al...

Alloying conducting channels for reliable neuromorphic computing.

A memristor has been proposed as an artificial synapse for emerging neuromorphic computing applications. To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform. An electrochemical metallization (ECM) memory, typically based on silicon (Si), has demonstrated a good analogue switching capability owing to the high mobil...

Pediatric Acute-Onset Neuropsychiatric Syndrome: A Data Mining Approach to a Very Specific Constellation of Clinical Variables.

Pediatric acute onset neuropsychiatric syndrome (PANS) is a clinically heterogeneous disorder presenting with: unusually abrupt onset of obsessive co...

Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.

Accurate estimation of reference evapotranspiration (ET) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation...

Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics.

Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching...

Accurate and efficient sequential ensemble learning for highly imbalanced multi-class data.

Multi-class classification for highly imbalanced data is a challenging task in which multiple issues must be resolved simultaneously, including (i) ac...

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses.

Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain....

Deep learning-based classification of rectal fecal retention and analysis of fecal properties using ultrasound images in older adult patients.

AIM: The present study aimed to analyze the use of machine learning in ultrasound (US)-based fecal retention assessment.

Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis.

Deep convolutional neural network (DCNN), now popularly called artificial intelligence (AI), has shown the potential to improve over previous computer...

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radio...

Probing the degradation of pharmaceuticals in urine using MFC and studying their removal efficiency by UPLC-MS/MS.

Nutrient recovery from source-separated human urine has attracted interest as it is rich in nitrogen and phosphorus that can be utilized as fertilizer...

Matching patients to clinical trials using semantically enriched document representation.

Recruiting eligible patients for clinical trials is crucial for reliably answering specific questions about medical interventions and evaluation. Howe...

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when r...

One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis.

Determining the number of factors is one of the most crucial decisions a researcher has to face when conducting an exploratory factor analysis. As no ...

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb.

The retention of a capsule endoscope (CE) in the stomach and the duodenal bulb during the examination is a troublesome problem, which can make the med...

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

In recent years, protein-ligand interaction scoring functions derived through machine-learning are repeatedly reported to outperform conventional scor...

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and...

A randomized controlled study incorporating an electromechanical gait machine, the Hybrid Assistive Limb, in gait training of patients with severe limitations in walking in the subacute phase after stroke.

Early onset, intensive and repetitive, gait training may improve outcome after stroke but for patients with severe limitations in walking, rehabilitat...

Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation.

Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically ...

Efficient treatment of outliers and class imbalance for diabetes prediction.

Learning from outliers and imbalanced data remains one of the major difficulties for machine learning classifiers. Among the numerous techniques dedic...