scholarly journals A Virtual Monochromatic Imaging Method for Spectral CT Based on Wasserstein Generative Adversarial Network With a Hybrid Loss

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110992-111011 ◽  
Author(s):  
Zaifeng Shi ◽  
Jinzhuo Li ◽  
Huilong Li ◽  
Qixing Hu ◽  
Qingjie Cao
Keyword(s):  
Imaging Method ◽  
Spectral Ct ◽  
Generative Adversarial Network ◽  
Virtual Monochromatic Imaging ◽  
Monochromatic Imaging ◽  
Adversarial Network

An Inhomogeneous Background Imaging Method Based on Generative Adversarial Network

2020 ◽  
Vol 68 (11) ◽  
pp. 4684-4693
Author(s):  
Xiuzhu Ye ◽  
Yukai Bai ◽  
Rencheng Song ◽  
Kuiwen Xu ◽  
Jianping An
Keyword(s):  
Imaging Method ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sara Imboden ◽  
Xuanqing Liu ◽  
Brandon S. Lee ◽  
Marie C. Payne ◽  
Cho-Jui Hsieh ◽  
...  
Keyword(s):  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
High Throughput Screening ◽  
Imaging Method ◽  
Significant Heterogeneity ◽  
Label Free ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Integrative Assessment ◽  
Conventional Cell

AbstractMesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a standard approach is to measure marker expression on the protein level via immunochemistry assays. Performing such measurements non-invasively and at scale has remained challenging as conventional methods such as flow cytometry and immunofluorescence microscopy typically require cell fixation and laborious sample preparation. Here, we developed an artificial intelligence (AI)-based method that converts transmitted light microscopy images of MSCs into quantitative measurements of protein expression levels. By training a U-Net+ conditional generative adversarial network (cGAN) model that accurately (mean $$r_s$$ r s = 0.77) predicts expression of 8 MSC-specific markers, we showed that expression of surface markers provides a heterogeneity characterization that is complementary to conventional cell-level morphological analyses. Using this label-free imaging method, we also observed a multi-marker temporal-spatial fluctuation of protein distributions in live MSCs. These demonstrations suggest that our AI-based microscopy can be utilized to perform quantitative, non-invasive, single-cell, and multi-marker characterizations of heterogeneous live MSC culture. Our method provides a foundational step toward the instant integrative assessment of MSC properties, which is critical for high-throughput screening and quality control in cellular therapies.

scholarly journals RRG-GAN Restoring Network for Simple Lens Imaging System

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3317
Author(s):  
Xiaotian Wu ◽  
Jiongcheng Li ◽  
Guanxing Zhou ◽  
Bo Lü ◽  
Qingqing Li ◽  
...  
Keyword(s):  
Imaging System ◽  
Optimization Theory ◽  
Computational Imaging ◽  
Imaging Method ◽  
Generative Adversarial Network ◽  
Application Performance ◽  
Adversarial Network ◽  
Convex Lens ◽  
Evaluation Algorithm ◽  
Experimental Comparisons

The simple lens computational imaging method provides an alternative way to achieve high-quality photography. It simplifies the design of the optical-front-end to a single-convex-lens and delivers the correction of optical aberration to a dedicated computational restoring algorithm. Traditional single-convex-lens image restoration is based on optimization theory, which has some shortcomings in efficiency and efficacy. In this paper, we propose a novel Recursive Residual Groups network under Generative Adversarial Network framework (RRG-GAN) to generate a clear image from the aberrations-degraded blurry image. The RRG-GAN network includes dual attention module, selective kernel network module, and residual resizing module to make it more suitable for the non-uniform deblurring task. To validate the evaluation algorithm, we collect sharp/aberration-degraded datasets by CODE V simulation. To test the practical application performance, we built a display-capture lab setup and reconstruct a manual registering dataset. Relevant experimental comparisons and actual tests verify the effectiveness of our proposed method.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

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