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CSAST: Content self-supervised and style contrastive learning for arbitrary style transfer.

Neural networks : the official journal of the International Neural Network Society
Arbitrary artistic style transfer has achieved great success with deep neural networks, but it is still difficult for existing methods to tackle the dilemma of content preservation and style translation due to the inherent content-and-style conflict....

Is AI the way forward for reducing metal artifacts in CT? Development of a generic deep learning-based method and initial evaluation in patients with sacroiliac joint implants.

European journal of radiology
PURPOSE: To develop a deep learning-based metal artifact reduction technique (dl-MAR) and quantitatively compare metal artifacts on dl-MAR-corrected CT-images, orthopedic metal artifact reduction (O-MAR)-corrected CT-images and uncorrected CT-images ...

Rethinking Saliency Map: A Context-Aware Perturbation Method to Explain EEG-Based Deep Learning Model.

IEEE transactions on bio-medical engineering
Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically study how to explain EEG-based deep learning models. In this paper, we review the related works that attempt to explain EEG-...

Deep learning synthesis of cone-beam computed tomography from zero echo time magnetic resonance imaging.

Scientific reports
Cone-beam computed tomography (CBCT) produces high-resolution of hard tissue even in small voxel size, but the process is associated with radiation exposure and poor soft tissue imaging. Thus, we synthesized a CBCT image from the magnetic resonance i...

DBCU-Net: deep learning approach for segmentation of coronary angiography images.

The international journal of cardiovascular imaging
Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which mak...

Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria.

Medical & biological engineering & computing
Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the ...

An attention-based deep convolutional neural network for ultra-sparse-view CT reconstruction.

Computers in biology and medicine
X-ray Computed Tomography (CT) techniques play a vitally important role in clinical diagnosis, but radioactivity exposure can also induce the risk of cancer for patients. Sparse-view CT reduces the impact of radioactivity on the human body through sp...

MLF-IOSC: Multi-Level Fusion Network With Independent Operation Search Cell for Low-Dose CT Denoising.

IEEE transactions on medical imaging
Computed tomography (CT) is widely used in clinical medicine, and low-dose CT (LDCT) has become popular to reduce potential patient harm during CT acquisition. However, LDCT aggravates the problem of noise and artifacts in CT images, increasing diagn...

Phase Contrast Image Restoration by Formulating Its Imaging Principle and Reversing the Formulation With Deep Neural Networks.

IEEE transactions on medical imaging
Phase contrast microscopy, as a noninvasive imaging technique, has been widely used to monitor the behavior of transparent cells without staining or altering them. Due to the optical principle of the specifically-designed microscope, phase contrast m...

MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion.

IEEE transactions on medical imaging
Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifacts, denoising is largely studied both within the medical imaging community and beyond the community as a gener...