AIMC Topic: Artifacts

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A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.

Nature neuroscience
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth da...

Sharp U-Net: Depthwise convolutional network for biomedical image segmentation.

Computers in biology and medicine
The U-Net architecture, built upon the fully convolutional network, has proven to be effective in biomedical image segmentation. However, U-Net applies skip connections to merge semantically different low- and high-level convolutional features, resul...

Copula-Based Data Augmentation on a Deep Learning Architecture for Cardiac Sensor Fusion.

IEEE journal of biomedical and health informatics
In the wake of Big Data, traditional Machine Learning techniques are now often integrated in the clinical workflow. Despite more capable, Deep Learning methods are not equally accepted given their unsatiated need for great amounts of training data an...

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.

NeuroImage
Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the ...

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment.

Sensors (Basel, Switzerland)
Gamification is known to enhance users' participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various ra...

Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction.

Physics in medicine and biology
High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces...

Quality control stress test for deep learning-based diagnostic model in digital pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising du...

Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery.

BMC nephrology
BACKGROUND: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible ...

Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: A clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI.

Magnetic resonance imaging
OBJECTIVE: To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hol...

Denoising and Motion Artifact Removal Using Deformable Kernel Prediction Neural Network for Color-Intensified CMOS.

Sensors (Basel, Switzerland)
Image intensifiers are used internationally as advanced military night-vision devices. They have better imaging performance in low-light-level conditions than CMOS/CCD. The intensified CMOS (ICMOS) was developed to satisfy the digital demand of image...