Category Archives: Machine Learning
AWS re:Invent: How to Use Machine Learning and Other Technology to Make the Most of Your Data – Inc.
If your company isn't treating data like an asset, youcould bemissing out ona majorgrowth opportunity.
That's according to SwamiSivasubramanian, vice president of Amazon Machine Learning.Sivasubramanianwas speaking duringa keynote conversation ondata and machine learning WednesdayatAWS re:Invent,a conference forbusiness owners and other technical decision-makers hosted byAmazon Web Services in Las Vegas.
Sivasubramanian says there are three thingscompaniescan do to make the most of their data. Here's his advice.
1.Modernize your data infrastructure.
Too many companies still treat their data like it's the 1990s when they should be implementing a modern data strategy, according to Sivasubramanian.This applies to both storing your data and"putting your data to work," he says. In many cases,hiring an outside company tomanage your databasefor you can save resources and help ensure your operations run smoothly. Sivasubramanian adds that acloud-basedsolution will help ensure that your company'sdata--even the most obscure, infrequently used bits--can be easily accessed by your teams that need it.
Applying modern solutions like machine learningto your database can alsohelp you detect problems faster. For example, an applicationslowdown that might otherwise go undetected for dayscan be identified and diagnosed quickly with machine learning. It can also provide suggestions for fixing problems with your data,which can be time consuming and costly if you're still doing somanually.
2.Unify your data.
It's important to have whatSivasubramanian refers to as a"single source of truth" about your business. Ensuring that your teams are all looking at the same data can help your company make the most of it. Of course,this doesn't mean every teamshould have access to every piece of data; different teams can and should have different permissions and levels of access. What's important is that this data is consistently reported and recorded.
"Opportunities to transform your business with data exist all along the value chain," says Sivasubramanian."But creating such a solution requires companies to have a full picture and a single view of their customers and their business."
3.Find innovative uses foryour data.
Applying insights to your data can help you improve existing operationsor build entirely new ones.Sivasubramanian pointsto severalAWS customers that have benefited from applying machine learning and analytics to their data.Tyson Foods has usedcameras armed with computer vision to identify ways to reducewaste bycutting down on packaging. AndPinterest has usednatural language processing to create more accurate search engines that allow employees to find the information they need faster.
"Machine learning is improving customer experiences, creating more efficiencies, and spurring completely new innovations," saysSivasubramanian. "And having the right data strategy is the key to these innovations."
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AWS re:Invent: How to Use Machine Learning and Other Technology to Make the Most of Your Data - Inc.
A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation |…
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A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation |...
Mindtree has Earned the Al and Machine Learning on Microsoft Azure Advanced Specialization – PRNewswire
WARREN, N.J. and BANGALORE, India, Dec. 1, 2021 /PRNewswire/ -- Mindtree,a global technology services and digital transformation company, today announced it has earned the AI and Machine Learning on Microsoft Azure advanced specialization, a validation of a services partner's deep knowledge, extensive experience and proven success in enabling customer adoption of AI and implementing Azure solutions for machine learning life cycle and AI-powered apps.
Only partners that meet stringent criteria around customer success and staff skilling, as well as pass a third-party audit of their AI and machine learning technical practices, are able to earn the AI and Machine Learning on Microsoft Azure advanced specialization.
As the speed of business accelerates, organizations of every type and size are looking for ways to streamline processes and deliver simpler, faster, and smarter resources to help them keep up. Partners with the AI and Machine Learning on Microsoft Azure advanced specialization can give organizations the tools and knowledge to develop AI solutions on their terms, build AI into their mission-critical applications, and put responsible AI into action.
"We are excited to be among Microsoft's first service partners to have earned the AI and Machine Learning on Microsoft Azure advanced specialization," said Radhakrishnan Rajagopalan, Global Head, Customer Success, Data and Intelligence, Mindtree. "Organizations are looking for ways to maximize business impact and revenue through augmentation and automation. As a result, AI and Machine Learning are playing an increasingly vital role in helping them unlock the full power of data for improved agility, richer experiences, smarter decision-making and reduced time-to-market. This advanced specialization validates our ability to enable organizations to optimize their digital strategies and investments, strengthening our reputation as a preferred digital transformation partner."
Rodney Clark, Corporate Vice President, Global Partner Solutions, Channel Sales and Channel Chief at Microsoft, added,"AI and Machine Learning on Microsoft Azure advanced specialization highlights the partners who can be viewed as most capable when it comes to implementing Azure solutions for machine learning lifecycle and AI-powered apps.Mindtreeclearly demonstrated that they have both the skills and the experience to enabling customer adoption of AI and Machine Learning in Microsoft Azure advanced specialization."
About Mindtree
Mindtree [NSE: MINDTREE] is a global technology consulting and services company that enables enterprises across industries to drive superior competitive advantage, customer experiences and business outcomes by harnessing digital and cloud technologies. A digital transformation partner to more than 260 of the world's most pioneering enterprises, Mindtree brings extensive domain, technology and consulting expertise to help reimagine business models, accelerate innovation and maximize growth. As a socially and environmentally responsible business, Mindtree is focused on growth as well as sustainability in building long-term stakeholder value. Powered by more than 29,700 talented and entrepreneurial professionals across 24 countries, Mindtree a Larsen & Toubro Group company is consistently recognized among the best places to work.
To learn more, please visit http://www.mindtree.comor follow us @Mindtree_Ltd.
For more information, contact:[emailprotected]
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Mindtree has Earned the Al and Machine Learning on Microsoft Azure Advanced Specialization - PRNewswire
AFTAs 2021: Most innovative third-party technology vendor (AI, machine learning and analytics)Moody’s Analytics – www.waterstechnology.com
AFTAs 2021: Most innovative third-party technology vendor (AI, machine learning and analytics)Moodys Analytics - WatersTechnology.com OVERVIEW
Accurately measuring credit risk is made more difficult by current events unfolding in real time.
It is critical to get real-time insights, early warning signals, and forward-thinking analysis on emerging trends and patterns to market participants.
Monitoring activities requires hours of manual work daily.
The Moodys Analytics real-time news solutions are differentiated by a natural-language processing (NLP) engine that identifies people, entities, topics, and sentiments within story text with precision, and leverages machine learning and human review to maintain and validate model output.
Combining NewsEdge technology and Credit Sentiment Score analytics produced a news-based analytical model to provide intelligence on the credit health of a company earlier than models that rely on traditional financial indicators of credit decline.
In 2020, Moodys bought Acquire Media, a US-based aggregator and distributor of curated real-time news, multimedia, data, and alerts, which allowed Moodys to enhance its own solution suite with the addition of NewsEdge, which provides early warning signals and real-time insight to users.
Moodys Analytics is preparing to launch its next generation signal-based interface of NewsEdge in January 2022.
The next generation NewsEdge platform will provide multi-application signal-based intelligence. Expanded uses will encompass functions-based applications like trading or compliance and industry-based applications such as adverse events impacting business.
Moodys will pull together entity, reference, and alternative data, and combine them with news content to reveal actionable signals that answer the question, What does this signal mean? directly for the user.
The signal-based platform will continue to roll out in phases, utilizing next-generation platform technologies and will expand for high-demand applications such as for supply chain, compliance, and personnel.
As a leader in the credit ratings business, Moodys has shown a commitment to innovation for its credit technology and data products. Moodys Analytics combines in-house expertise and assets gained from strategic partnerships and acquisitions to deliver market participants an insightful, personalized news hub.
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Machine Learning Clarifies Stress-Based Degradation of Biosimilars – The Center for Biosimilars
Machine learning shows promise as a complementary approach to chromatographic (mixture separation) techniques for assessing biosimilarity and stability, according to a recent study.
Investigators evaluated machine learning vs chromatographic analysis in the study of 3 trastuzumab biosimilars and their reference product (Herceptin) under control and stress conditions. They concluded the machine learning results correlated with the chromatographic data and revealed patterns elucidating the effects of pH and thermal stress conditions.
Trastuzumab, a monoclonal antibody to human epidermal growth factor receptor 2 (HER2), is approved as a treatment for metastatic breast cancer, early breast cancer, and metastatic gastric cancer. The investigators found that the biosimilars showed high similarity under control conditions, but differences in degradation patterns were detected underforced degradation conditions in the study.
First, physicochemical characteristics of the reference product and biosimilar trastuzumab products (approved for use in Egypt; and referred to as B1, B2, and B3 in the study) were determined by size exclusion chromatography, cation exchange chromatography, and peptide mapping. The biologics were evaluated under control conditions and under pH and thermal stress. The investigators then used unsupervised machine learning techniques to find patterns in the chromatographic data.
Chromatographic Analysis
The authors said primary structure and size and charge variants are quality attributes expected to affect the quality, safety, and efficacy of biologic drugs including trastuzumab. These attributes were similar in the biosimilars and reference product under control conditions, the authors found.
Thermal and pH stress, the authors noted, are among the most studied stress conditions in forced degradation studies due to their direct effect on the size and charge variant profiles of [monoclonal antibodies] mAbs through deamidation and oxidation. Under thermal and pH stress, the investigators did find differences in the degradation of the different products.
Size variants
Based on size exclusion chromatography, B2 and B3 showed a tendency to form high- and low-molecular weight variants under acidic and basic stress, and B2 showed 83% degradation by the 2-week time point under acidic stress. Under thermal stress, B3 showed the greatest degradation, 39% after 2 weeks.
Charge variants
Under acidic stress, the products varied from 19.9% degradation of the main variant of the reference product at 2 weeks to 93% for B2. Under basic stress, all samples showed a comparable increase in abundance of acidic variants. Under thermal stress, the charge variant distribution of B2 and B3 were similar to charge variant distribution for the reference product, while B1 showed a greater abundance of acidic variants.
Principal Component Analysis
The investigators used unsupervised machine learning techniques, which find patterns in data with no prior training or predefined subcategories. Principal component analysis (PCA) is a method for reducing complexity in high-dimensional data to a small number of components that explain the greatest percentage of the variance in the data set.
The authors plotted size exclusion chromatography and cation exchange chromatography data on 2-dimensional coordinates representing the 2 components (PC1 and PC2) that explained the most variance to identify patterns in the data. Primary component analysis of chromatographic and peptide mapping data of the control samples showed no outliers, which the authors said supports biosimilarity of the products.
The plot of control and acidic stressed samples showed that the control samples were separated along the primary component 1 (PC1) axis, while the stressed samples were distributed along the PC2 axis. Samples of the same product were clustered relevantly close to each other, the authors said, and their PCA results on control and acidic-stressed samples suggested 41% of the variance in the data was due to the applied stress, and 25% was due to inherent differences in the chromatographic profiles of the products.
Clustering Analysis
The investigators also used 2 clustering techniques, k-means and density-based spatial clustering of applications with noise (DBSCAN), on the data from the top 2 PCs from their primary component analysis. According to the authors, cluster analysis is an unsupervised exploratory technique aiming to find natural grouping in data so that items in the same cluster are more similar to each other than to those from different clusters.
Due to the inherent variability and large number of possible structural variants of monoclonal antibodies, the authors said, machine learningaided approaches have great value for assessing their critical quality attributes. They cited previous research using PCA to reveal patterns in the data on biosimilarity and stability of other biologics, recombinant human growth hormone and infliximab.
K-means clustering of the unstressed samples segregated the products into 3 clusters, with the reference product and B2 each forming their own cluster, and B1 and B3 allocated to the same cluster. DBSCAN segregated each product to its own cluster.
K-means clustering was able to separate control and pH-stressed samples into different clusters, although B2 control samples were clustered with the stressed reference product and B3 samples. Cluster analysis suggested B3 was most similar to the reference product under acidic stress, while B2 was most similar under thermal stress, and all products had a similar response to basic pH stress. The greatest variability between control samples was between the reference product and B2.
Finally, application of principal component and clustering analyses to the collective data set from all the applied chromatographic techniques supported biosimilarity of the products, the authors said. This principal component analysis identified no samples that were significantly different from the others; k-means identified 3 clusters (reference product, B1 + B3, and B2), and DBSCAN identified 4 clusters, one containing each product.
The authors concluded their results supported the biosimilarity of the products analyzed, and highlighted that regarding the charge and size profiles of the studied products, B2 showed higher variability (than B1 and B3) compared to HC under both control and stress conditions. They said that the chromatographic fingerprints and machine learning results were correlated and were able to reveal patterns related to the effect of different stress conditions on the different investigated products. They recommended future studies explore other machine learning tools to interpret physicochemical data on biologic products.
For Further Reading
The European Medicines Authority reports on a pilot experiment in tailoring development of biosimilars, or eliminating unnecessary testing, and the World Health Organization develops guidelines to support the tailoring concept.
Reference
Shatat SM, Al-Ghobashy MA, Fathalla FA, Abbas SS, Eltanany BM. Coupling of trastuzumab chromatographic profiling with machine learning tools: a complementary approach for biosimilarity and stability assessment. J Chromatogr B Analyt Technol Biomed Life Sci. 2021;1184:122976. doi:10.1016/j.jchromb.2021.122976
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Machine Learning Clarifies Stress-Based Degradation of Biosimilars - The Center for Biosimilars
Artificial Intelligence, Machine Learning, and Biometric Security Technology will be Drivers of Digital Transformation in 2022 And Beyond: IEEE…
Published on November 25, 2021
Bengaluru IEEE, the worlds largest technical professional organization committed to advancing technology for humanity, today concluded its virtual roundtable focused on The Next Big Thing in Technology, the top technologies that will have a massive impact in 2022 and beyond. With the ongoing COVID-19 pandemic where digitization and technology have become increasingly powerful drivers for innovation, IEEE curated this roundtable to discuss how AI, ML, and advanced security mechanisms are fuelling industries to drastically increase productivity, automate systems to achieve better accuracy, and help workforces outperform while minimizing tedious repetitive tasks. AI-driven learning systems are generating more opportunities for intertwining technology trends which will only continue in 2022.
Speaking in the roundtable about The Impact of Technology in 2022, Sukanya Mandal, IEEE Member, and Founder and Data Science Professional explained, AI and ML are creating strides for technological advancements and will be extremely vital for our future to increase output, bring specialization into job roles, and increase the importance of human skills such as problem-solving, quantitative skills, and creativity. I strongly believe the future will consist of people and machines working together to improve and adapt to a modern way of working. AI will also play a critical role in all aspects of e-commerce, from customer experiences and marketing to fulfillment and distribution.
Recently published research on Artificial Intelligence and the Future of Work conducted by MIT Work of The Future, highlights that AI continues to push large-scale innovation, create more jobs, advance labor processes, and holds the immense potential to impact various sectors. Furthermore, a Gartner report predicts that half of data centers around the world will deploy advanced robotics with AI and ML capabilities by 2025, which is estimated to lead to 30% higher operating efficiencies.
Industry 4.0 is all about interconnecting machines, processes, and systems for maximum process optimization. Along the same lines, Industry 5.0 will be focused on the interaction between humans and machines. It is all about recognizing human expertise and creatively interconnecting with machine intelligence for process optimization. It is true to say that we are not far away from the 5th industrial revolution. Over this decade and the next, we will witness applications of IoT and smart systems adhering to the principles of the 5th industrial revolution across various sectors., she further added.
The roundtable also focused on Redefining the Future of Biometric Security Technology. AI-Machine Learning-based systems, in collaboration with the latest technologies such as IoT, Cloud Computing, and Data Science, have successfully advanced Biometrics. Biometric systems generate huge volumes of data that can be managed with Machine Learning techniques for better handling and space management. Deep learning can also play a vital role in analyzing data to build automated systems that achieve better accuracy. A report by Carnegie Endowment for International Peace stated that 75 countries, representing 43 percent of a total of 176 countries, are actively leveraging AI capabilities for biometric purposes, including facial recognition systems, smart cities, and others.
Commenting on this, Sambit Bakshi, Senior IEEE Member, said, During the pandemic, we all saw the increased use of technology in public places such as airports, train stations, etc., not only to monitor body temperatures but also to help maintain COVID protocols. Biometric technologies are rapidly becoming a part of the daily lives of people around the world.
Biometric authentication is likely to expand in the coming years. Multimodal authentication exercises a combination of similar biometric technologies to authenticate someone. Cues from different platforms can be integrated through cloud computing and IoT-based architecture to verify someones identity. These can include gait features or anthropometric signatures. The future of biometric security lies in simplicity. Improving modern techniques is the simplest way to offer a high level of protection.
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Artificial Intelligence, Machine Learning, and Biometric Security Technology will be Drivers of Digital Transformation in 2022 And Beyond: IEEE...
ExoMiner Goes Planet Hunting! NASA’s Machine Learning Network Validates 301 New Exoplanets at One Go | The Weather Channel – Articles from The Weather…
This artist's illustration shows the planetary system K2-138, which was discovered by citizen scientists in 2017 using data from NASA's Kepler space telescope.
After the first exoplanet was identified almost three decades earlier, in 1992, humanity has come a long way in terms of exoplanet discovery. As of today, we have spotted over 4000 validated exoplanets that revolve around their respective suns.
Exoplanets are celestial bodies that exist outside our vast solar system. Equipped with cutting-edge technology, many research groups have been identifying these exoplanets left, right and centre.
However, for the first time ever, 301 validated planets were added to the ever-growing exoplanet tally all at once!
Wondering how? The US space agency NASA reported that a new deep neural network called 'ExoMiner' was responsible for this incredible scientific feat.
The ExoMiner leverages NASA's Pleiades supercomputer and, like any deep neural network, can automatically learn a task when provided with enough data. ExoMiner is designed with various tests, properties human experts use to confirm new exoplanets, past confirmed exoplanets, and false-positive cases in mind. Thus, it could tell apart actual exoplanets from imposters, making this technology and its predictions highly reliable.
"Unlike other exoplanet-detecting machine learning programs, ExoMiner isn't a black boxthere is no mystery as to why it decides something is a planet or not," said Jon Jenkins, an exoplanet scientist at NASA's Ames Research Center in California's Silicon Valley. "We can easily explain which features in the data lead ExoMiner to reject or confirm a planet."
It is a highly tedious process to comb vast datasets from missions like Kepler, which has hundreds of stars in its range of view, each with the potential to house numerous possible exoplanets. In such cases, the ExoMiner is the perfect substitute as it reduces the burden of astronomers in sifting through data and determining what is and isn't a planet.
"When ExoMiner says something is a planet, you can be sure it's a planet," said Hamed Valizadegan, ExoMiner project lead and machine learning manager with the Universities Space Research Association at Ames. "ExoMiner is highly accurate and in some ways more reliable than both existing machine classifiers and the human experts it's meant to emulate because of the biases that come with human labelling."
NASA said that all 301 machine-validated planets were originally detected by the Kepler Science Operations Center and were promoted to planet candidate status by the Kepler Science Office. But until ExoMiner, no one was able to validate them as planets.
And while none of the newly discovered planets is thought to be Earth-like or in their parent stars' habitable zones, they share some traits with the rest of the verified exoplanet population in our galaxy.
According to Jon Jenkins, the 301 discoveries will help researchers better understand planets and solar systems beyond our own and what makes ours so unique.
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ExoMiner Goes Planet Hunting! NASA's Machine Learning Network Validates 301 New Exoplanets at One Go | The Weather Channel - Articles from The Weather...
BIS: What Does Machine Learning Say About The Drivers Of Inflation? – Exchange News Direct
SummaryFocus
Which are the key drivers of inflation, and what role do expectations play in the inflation process have been long-standing questions in macroeconomics, particularly given their relevance to economic policymaking. This paper sheds some fresh light on these central questions using machine learning.
I examine inflation in 20 advanced economies since 2000 through the lens of a flexible data-driven method. Beyond comparing explanatory performance with more traditional econometric methods, as far as possible, I also interpret the predicted relations between explanatory variables and consumer price inflation.
The machine learning model predicts headline and core CPI inflation relatively well, even when only a small standard set of macroeconomic indicators is used. Inflation prediction errors are smaller than with standard OLS models using the same set of explanatory variables which are firmly grounded on economic theory. Expectations emerge as the most important predictor of CPI inflation. That said, the relative importance of expectations has declined during the last 10 years.
This paper examines the drivers of CPI inflation through the lens of a simple, but computationally intensive machine learning technique. More specifically, it predicts inflation across 20 advanced countries between 2000 and 2021, relying on 1,000 regression trees that are constructed based on six key macroeconomic variables. This agnostic, purely data driven method delivers (relatively) good outcome prediction performance. Out of sample root mean square errors (RMSE) systematically beat even the in-sample benchmark econometric models, with a 28% RMSE reduction relative to a nave AR(1) model and a 8% RMSE reduction relative to OLS. Overall, the results highlight the role of expectations for inflation outcomes in advanced economies, even though their importance appears to have declined somewhat during the last 10 years.
Keywords: expectations, forecast, inflation, machine learning, oil price, output gap, Phillips curve.
JEL classification: E27, E30, E31, E37, E52, F41.
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BIS: What Does Machine Learning Say About The Drivers Of Inflation? - Exchange News Direct
Machine learning can improve your public services. Are you ready to take the red pill? – The Register
Paid Post Theres no doubt that machine learning has massive potential for improving the development, delivery, and operation of public services, whether thats delivering insights into disease proliferation, enabling predictive maintenance, or identifying fraud.
This can seem to be a complex technology. But if the principles behind machine learning can be intimidating, they are nowhere near as intimidating as the consequences of getting it wrong and generating questionable or even positively harmful outcomes.
So, whether your organisation is preparing for its first journey with machine learning, or has already implemented the technology, it pays to step back and take a broader look.
And we have something that can help you in this process, in the shape of Machine Learning Reloaded, an in-depth dive into the principles and applications of machine learning in public services.
This concise but info-packed report is part of the Perspectives series from our chums at global smart software specialists Civica, with their latest volume produced in association with the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent Artificial Intelligence (ART-AI) at the University of Bath.
Machine Learning Reloaded gives you a crash course in the principles behind the tech, helping you understand what it is, what it isnt, and what it can potentially do.
It also provides an in-depth examination of how machine learning is making a difference across the full range of public services, including local government, health and care, government and justice, housing, and education.
With best use cases, and explanations of key applications, it will take you direct to a range of other resources showing how public bodies have already put the technology to work.
As well as whetting your appetite, it provides you with a template for planning your own machine learning projects, from choosing your data, selecting your tools, and choosing the right personnel and partners while guiding you on how to do all this ethically and responsibly.
Civicas NorthStar lab has already run the ruler over Chatbots, and Immersive Technologies. You can find out more and explore the rest of the Perspectives from Civica series at http://www.civica.com/perspectives. If you want to know where cutting edge technology is taking public services, this is the place to start.
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Machine learning can improve your public services. Are you ready to take the red pill? - The Register
Less energy, better quality PAM images with machine learning – The Source – Washington University in St. Louis – Washington University in St. Louis…
Photoacoustic microscopy (PAM) allows researchers to see the smallest vessels inside a body, but it can generate some unwanted signals or noise. A team of researchers at the McKelvey School of Engineering at Washington University in St. Louis found a way to significantly reduce the noise and maintain image quality while reducing the laser energy needed to generate images by 80%.
Song Hu, associate professor of biomedical engineering, and members of his lab devised this new method using a machine-learning-based image processing technique, called sparse coding, to remove the noise from PAM images of vessel structure, oxygen saturation and blood flow in a mouse brain. Results of the work were published online inIEEE Transactions on Medical ImagingNov. 1.
To acquire such images, the researchers need a dense sampling of data, which requires a high laser pulse repetition rate that may raise safety concerns. Reducing the laser pulse energy, however, leads to impaired image quality and inaccurate measurement of blood oxygenation and flow. Thats where Zhuoying Wang, a doctoral student in Hus lab and first author of the paper, brought in sparse coding, a type of machine learning often used in image processing that doesnt need a ground truth on which to train, to improve the image quality and quantitative accuracy while using low laser doses.
The team applied the technique to images of blood hemoglobin concentration, oxygenation and flow in a mouse brain at both normal and reduced energy levels. Their two-step approach performed very well, significantly reducing the noise and achieving similar image quality that was previously only possible with five times higher laser energy.
In the first step of our approach, sparse coding separated the vascular signals from noise in the cross-sectional scans acquired at different tissue locations, called B-scans, because the noise is less sparse than the signals, Wang said. Then we applied the same sparse coding strategy on the projection image formed by denoised B-scans in the second step to further suppress the background noise.
Hu said while machine learning has been previously used to denoise photoacoustic images, their two-step method is a step ahead.
Our approach allows us to remove the noise and leave the signal intact, Hu said. It not only provides higher visibility of the microvessels but also preserves the signal presentation to give us the opportunity to do quantitative imaging.
While this is the initial demonstration of what these machine learning tools can do, Hu said it shows the importance of advanced computational tools in imaging in general and in photoacoustic microscopy in particular.
The five-times reduction in laser energy is promising, but we think we could do more with follow-up advances, not only to reduce the laser energy but also to improve the temporal resolution, or how fast we can take the image without losing resolution and spatial coverage, he said.
This research was supported by the National Institutes of Health (NS099261 and NS120481), the National Science Foundation (2023988), and the Chan Zuckerberg Initiative DAFan advised fund of Silicon Valley Community Foundation (2020-226174). Z. Wang is supported by the Washington University Imaging Sciences Pathway Fellowship.
Wang Z, Zhou Y, Hu S. Sparse Coding-enabled Low-fluence Multi-parametric Photoacoustic Microscopy.IEEE Transactions on Medical Imaging, early access online Nov. 1, 2021. DOI:10.1109/TMI.2021.3124124
The McKelvey School of Engineering at Washington University in St. Louis promotes independent inquiry and education with an emphasis on scientific excellence, innovation and collaboration without boundaries. McKelvey Engineering has top-ranked research and graduate programs across departments, particularly in biomedical engineering, environmental engineering and computing, and has one of the most selective undergraduate programs in the country. With 140 full-time faculty, 1,387 undergraduate students, 1,448 graduate students and 21,000 living alumni, we are working to solve some of societys greatest challenges; to prepare students to become leaders and innovate throughout their careers; and to be a catalyst of economic development for the St. Louis region and beyond.
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Less energy, better quality PAM images with machine learning - The Source - Washington University in St. Louis - Washington University in St. Louis...