Combining Monte Carlo with Deep Learning: Predicting High-resolution, Low-noise Dose Distributions Using a Generative Adversarial Network for Fast and Precise Monte Carlo Simulations

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss

IEEE Access ◽

10.1109/access.2021.3099695 ◽

2021 ◽

pp. 1-1

Author(s):

Guangyuan Li ◽

Jun Lv ◽

Xiangrong Tong ◽

Chengyan Wang ◽

Guang Yang

Keyword(s):

High Resolution ◽

Generative Adversarial Network ◽

Pelvic Mri ◽

Adversarial Network ◽

Mri Reconstruction

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Exploring the robustness of Keplerian signals to the removal of active and telluric features

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3180 ◽

2020 ◽

Vol 500 (1) ◽

pp. 548-557

Author(s):

M Lisogorskyi ◽

H R A Jones ◽

F Feng ◽

R P Butler ◽

S Vogt

Keyword(s):

Monte Carlo ◽

High Resolution ◽

Monte Carlo Simulations ◽

Large Amplitude ◽

Absorption Cell ◽

Stellar Rotation ◽

Long Period ◽

Active Line ◽

Low Amplitude ◽

Rotation Signal

ABSTRACT We examine the influence of activity- and telluric-induced radial velocity (RV) signals on high-resolution spectra taken with an iodine absorption cell. We exclude 2-$\mathring{\rm A}$ spectral chunks containing active and telluric lines based on the well-characterized K1V star α Centauri B and illustrate the method on Epsilon Eridani – an active K2V star with a long-period, low-amplitude planetary signal. After removal of the activity- and telluric-sensitive parts of the spectrum from the RV calculation, the significance of the planetary signal is increased and the stellar rotation signal disappears. In order to assess the robustness of the procedure, we perform Monte Carlo simulations based on removing random chunks of the spectrum. Simulations confirm that the removal of lines impacted by activity and tellurics provides a method for checking the robustness of a given Keplerian signal. We also test the approach on HD 40979, which is an active F8V star with a large-amplitude planetary signal. Our Monte Carlo simulations reveal that the significance of the Keplerian signal in the F star is much more sensitive to wavelength. Unlike the K star, the removal of active lines from the F star greatly reduces the RV precision. In this case, our removal of a K star active line from an F star does not a provide a simple useful diagnostic because it has far less RV information and heavily relies on the strong active lines.

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AN AI-BASED APPROACH TO ENHANCED FRACTURE RESOLUTION IN IMAGE LOGS

10.30632/spwla-2021-0081 ◽

2021 ◽

Author(s):

James Howard ◽

◽

Joe Tracey ◽

Mike Shen ◽

Shawn Zhang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Nearest Neighbor ◽

Rock Fracture ◽

Short Interval ◽

Acoustic Properties ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Deep Learning Neural Network ◽

Borehole Image

Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.

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Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽

10.3390/s18113913 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3913 ◽

Cited By ~ 6

Author(s):

Mingxuan Li ◽

Ou Li ◽

Guangyi Liu ◽

Ce Zhang

Keyword(s):

Deep Learning ◽

Cognitive Radio ◽

Supervised Learning ◽

Rapid Development ◽

Generative Adversarial Networks ◽

Modulation Recognition ◽

Learning Methods ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

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