AIMC Topic: Signal-To-Noise Ratio

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Fluorescence microscopy datasets for training deep neural networks.

GigaScience
BACKGROUND: Fluorescence microscopy is an important technique in many areas of biological research. Two factors that limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging and, when ima...

On training targets for deep learning approaches to clean speech magnitude spectrum estimation.

The Journal of the Acoustical Society of America
Estimation of the clean speech short-time magnitude spectrum (MS) is key for speech enhancement and separation. Moreover, an automatic speech recognition (ASR) system that employs a front-end relies on clean speech MS estimation to remain robust. Tra...

PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning.

Clinical nuclear medicine
PURPOSE: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans.

Deep learning for in vivo near-infrared imaging.

Proceedings of the National Academy of Sciences of the United States of America
Detecting fluorescence in the second near-infrared window (NIR-II) up to ∼1,700 nm has emerged as a novel in vivo imaging modality with high spatial and temporal resolution through millimeter tissue depths. Imaging in the NIR-IIb window (1,500-1,700 ...

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging.

Journal of X-ray science and technology
The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a low radiation dose in CT can result in severe artifacts and noise in the captured images, thus affect...

Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A Survey.

Current medical imaging
OBJECTIVE: Several denoising methods for medical images have been applied, such as Wavelet Transform, CNN, linear and Non-linear methods.

Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution.

Cerebral cortex (New York, N.Y. : 1991)
Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatia...

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network.

Medicine
We have developed a deep learning-based approach to improve image quality of single-shot turbo spin-echo (SSTSE) images of female pelvis. We aimed to compare the deep learning-based single-shot turbo spin-echo (DL-SSTSE) images of female pelvis with ...

High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning.

Journal of biomedical optics
SIGNIFICANCE: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead ...

Leukocyte super-resolution via geometry prior and structural consistency.

Journal of biomedical optics
SIGNIFICANCE: Researchers have made great progress in single-image super-resolution (SISR) using deep convolutional neural networks. However, in the field of leukocyte imaging, the performance of existing SISR methods is still limited as it fails to ...