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uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis for breast cancer use pathologist-trained deep learning algorithms to enable quick calculation of Ki-67, ER and PR tumour cell nuclei positivity. This includes a whole slide analysis workflow with automated pre-computing of the slide image prior to pathologist assessment, and a clear visual overlay highlighting tumour cells with and without nuclear staining. uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis for breast cancer produce actionable assessments of scanned slide images that are objective and reproducible, aiding pathologists in quantification of these breast cancer markers.

Intended for use with Roche's high medical value assays and slides stained on a BenchMark ULTRA instrument using ultraView DAB detection kit, the uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis algorithms are ready-to-use and integrated within Roche's uPath enterprise software and NAVIFYDigital Pathology, the cloud version of uPath. These algorithms are for Research Use Only. Not for use in diagnostic procedures.

"Roche is committed to the expansion of digital pathology solutions to address unmet medical needs and breast cancer diagnostics is a key opportunity area. Innovations like image analysis algorithms have the potential to impact patient care by increasing the information available to pathologists and enhancing diagnostic confidence," said Jill German, Head of Roche Diagnostics Pathology Customer Area.

In December 2021, Roche will be presenting an abstract (# P1-02-17) on our artificial intelligence, deep learning development of our breast panel RUO algorithms at the San Antonio Breast Cancer Symposium, which features the latest research and development on breast cancer research. To find out more about this symposium, visit https://www.sabcs.org/Symposium-Overview-2021.

About Roche Digital Pathology

As the leading provider of pathology lab solutions, Roche is delivering an end-to-end digital pathology solution from tissue staining to producing high-quality digital images that can be reliably assessed using automated clinical image analysis algorithms.

Whole slide imaging combined with modern artificial intelligence (AI)-based image analysis tools have the potential to transform the practice of pathology. The use of AI and deep learning methods to interpret whole slide images in digital pathology enables pathologists to derive novel and meaningful diagnostic insights from tissue samples. AI-based image analysis automates quantitative tasks and enables fast, repeatable evaluation of information-rich tissue images that are sometimes difficult to interpret manually. AI-based image analysis uncovers aspects that are invisible to the human eye and reduces the risk of human error. Patients, whose tissue samples are analysed using AI-based image analysis, can benefit from a faster and more accurate diagnosis with IVD products. The insights gained from these analyses can help pathologists determine the best treatment option for cancer patients.

Roche offers two deployment options for its uPath software: an on-premise solution and a cloud solution, marketed as NAVIFY Digital Pathology. The VENTANA DP 200 slide scanner and Roche uPath enterprise software are CE-IVD marked for in-vitro diagnostic use and are available in the U.S. for Research Use Only (RUO). Not for use in diagnostic procedures. Image analysis algorithms developed by third-party entities and their utilisation are the responsibility of the third party provider.

About breast cancerBreast cancer is the second most common cancer in the world, with an estimated 2.3 million new cancer cases diagnosed in 20201 (12% of all cancers) and is the most common cancer in women globally,. (see references at bottom)

About RocheRoche is a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people's lives. The combined strengths of pharmaceuticals and diagnostics, as well as growing capabilities in the area of data-driven medical insights help Roche deliver truly personalised healthcare. Roche is working with partners across the healthcare sector to provide the best care for each person.

Roche is the world's largest biotech company, with truly differentiated medicines in oncology, immunology, infectious diseases, ophthalmology and diseases of the central nervous system. Roche is also the world leader in in vitro diagnostics and tissue-based cancer diagnostics, and a frontrunner in diabetes management. In recent years, Roche has invested in genomic profiling and real-world data partnerships and has become an industry-leading partner for medical insights.

Founded in 1896, Roche continues to search for better ways to prevent, diagnose and treat diseases and make a sustainable contribution to society. The company also aims to improve patient access to medical innovations by working with all relevant stakeholders. More than thirty medicines developed by Roche are included in the World Health Organization Model Lists of Essential Medicines, among them life-saving antibiotics, antimalarials and cancer medicines. Moreover, for the thirteenth consecutive year, Roche has been recognised as one of the most sustainable companies in the Pharmaceuticals Industry by the Dow Jones Sustainability Indices (DJSI).

The Roche Group, headquartered in Basel, Switzerland, is active in over 100 countries and in 2020 employed more than 100,000 people worldwide. In 2020, Roche invested CHF 12.2 billion in R&D and posted sales of CHF 58.3 billion. Genentech, in the United States, is a wholly owned member of the Roche Group. Roche is the majority shareholder in Chugai Pharmaceutical, Japan. For more information, please visit http://www.roche.com

VENTANA, BENCHMARK, ultraView and UPATH are trademarks of Roche. Other product names and trademarks are the property of their respective owners.

Derrick Fennell Roche Tissue Diagnostics Communications 520-203-1757 [emailprotected]

SOURCE Roche

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Roche announces the release of its newest artificial intelligence based digital pathology algorithms to aid pathologists in evaluation of breast...

In light of a recent Cybereason research report, Organizations at Risk: Ransomware Attackers Dont Take Holidays, regarding the prevalence of ransomware attacks that occur during off-hours, its imperative that we look towards robust AI security solutions, such that you can know your organization is protected even when none of your staff is online.

Effective defense against the holistic issue of ransomware is much larger than just a technology problem. At its highest level, the fight against ransomware is a race against time. From the moment an attacker enters your environment, they stay there as long as necessary engaging in subtle yet detectable activity long before encrypting any of your data.

The reason for this is that ransomware is financially motivated, meaning that attackers want to exfiltrate enough sensitive information and infiltrate as many systems as they can in order to demand the highest ransom possible, making their attack operations very methodical and intentional.

Predictive Ransomware Protection, a revolutionary AI-based endpoint protection solution, detects attacks at the earliest stages in real-time by bringing artificial intelligence to each and every endpoint.

In order for your organization to effectively defend against complex ransomware attacksor RansomOpssophisticated and artificially intelligent endpoints are required. An effective solution employs a combination of AI-Powered NGAV Engines and File Manipulation Detection.

Its no secret that legacy AV just doesnt hold up anymore. Ransomware attackers themselves use artificial intelligence and highly sophisticated tools to automate aspects of their attacks, and new ransomware strains or re-packed binaries are continually developed that render traditional antivirus solutions useless.

Next-generation antivirus goes beyond simply monitoring for already known Indicators of Compromise (IOCs) by detecting attacks based on Indicators of Behavior (IOBs), the more subtle chains of potentially malicious behavior.

This is critical because the complex RansomOps of today, as mentioned above, are multifaceted operations which occur in distinct phases that only an AI-powered behavioral analytics solutions like NGAV can prevent and detect early in the attack chain.

NGAV includes several layers of protection to address each of the following types of threats:

All ransomware strains are forms of malware (however, not all malware are ransomware); highly effective malware preventionsuch as that provided by AI-Powered NGAV Engineshas proven to be a formidable preventative control against commoditized and never before seen ransomware strains.

In some novel cases, attackers will be able to bypass the multi-layered defenses of advanced NGAV. When an attacker is able to get far enough in their campaign to begin performing encryption, it is important that a solution provides a fail-safe to prevent wide-spread encryption and thus eliminating the attackers ability to demand a ransom.

File Manipulation Detection provides a means to predict and respond to an attack before it propagates far enough to disrupt business operations. This technique analyzes files at the kernel layerbelow the operating systemsuch that it is able to detect initiation of the encryption process of a file at the most fundamental level.

With this deep visibility extending as far as the binary level for each file, machine learning algorithms provide a combination of novel and sophisticated techniques such as Natural Language Detection, Binary Similarity Analysis, and extension-alteration identification and other advanced approaches to battling encryption.

By evaluating the structural make-up of the document contents, Natural Language Detection identifies when the written sentences in a file are becoming jumbled, indicating the earliest signs of encryption.

Binary Similarity Analysis leverages a technique known as fuzzy matching, meaning that it calculates a significant level of difference between file contents that enables malicious alterations to be identified. Monitoring files at the binary level enables this analysis to detect when the contents of the file are being randomized, indicating malicious activity.

Modern cybersecurity solutions, such as Predictive Ransomware Protection, provide a significant advantage over other endpoint protection approaches by employing Global File Manipulation Detection to protect local and network files. Solutions that cannot deliver artificial intelligence at the endpoint leave an organization vulnerable to mass encryption. With kernel-level visibility, a full scale ransomware attack can be predicted and prevented, ensuring that business operations can continue without interruption.

The Cybereason Predictive Ransomware Protection solution is unmatched in the industry, and is why Cybereason remains undefeated in the fight against ransomware. Learn more about ransomware defense here or schedule a demo today to learn how your organization can stay undefeated.

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Under the Hood: What Artificial Intelligence on the Endpoint Looks Like - Security Boulevard

Officials at the Department of Veterans Affairs are looking to increase efficiency and optimize their clinicians professional capabilities, featuring advanced artificial intelligence and machine learning technologies.

In a November presolicitation, the VA seeks to gauge market readiness for advanced healthcare device manufacturing, ranging from prosthetic solutions, surgical instruments, and personalized digital health assistant technology, as well as artificial intelligence and machine learning capabilities.

Dubbed Accelerating VA Innovation and Learning, or AVAIL, the program is looking to supplement and support agency health care operations, according to Amanda Purnell, an Innovation Specialist with the VA

What we are trying to do is utilize AI and machine learning to remove administrative burden of tasks, she told Nextgov.

The technology requested by the department will be tailored to areas where a computer can do a better, more efficient job than a human, and thereby give people back time to complete demanding tasks that require human judgement.

Some of these areas the AI and machine learning technology could be implemented include surgical preplanning, manufacturing submissions, and 3D printing, along with injection molding to produce plastic medical devices and other equipment.

Purnell also said that the VA is looking for technology that can handle the bulk of document analyses. Using machine learning and natural language processing to scan and detect patterns in medical images, such as CT scans, MRIs and dermatology scans is one of the ways the VA aims to digitize its administrative workload.

Staff at the VA is currently tasked with looking through faxes and other clinical data to siphon it to the right place. AVAIL would combine natural language processing to manage these operations and add human review when necessary.

Purnell said that the forthcoming technology would emphasize streamlining processes that are better and faster done by machines and allowing humans to do something that is more kind of human-meaningful, and also allowing clinicians to operate to the top of their license.

She noted that machines are highly adept at scanning and analyzing images with AI. The VA procedure would likely have the AI technology to do a preliminary scan, followed by a human clinician to make their expert opinion based on results.

With machine learning handling the bulk of these processes along with other manufacturing and designing needs, clinicians and surgeons within the VA could focus more on applying their medical and surgical skills. Purnell used the example of a prosthetist getting more time to foster a human connection with a client rather than oversee other health care devices and manufacturing details.

It is making sure humans are used to their best advantage, and that were using technology to augment the human experience, she said.

The AVAIL program also stands to improve the ongoing modernization effort of the VAs beleaguered electronic health record (EHR) system, which has suffered deployment hiccups thanks to difficult interfaces and budget constraints.

The AI and machine learning technology outlined in the presolicitation could also support new EHR infrastructure and focus on an improved user experience, mainly with an improved platform interface and other accessibility features.

Purnell underscored that having AI manage form processing and data sharing capabilities, including veteran claims and benefits, is another beneficial use case.

Were alleviating that admin burden and increasing the experience both for veterans and our clinicians, in that veterans are getting more facetime with our clinicians and clinicians are doing more of what they are trained to do, Purnell said.

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VA Aims To Reduce Administrative Tasks With AI, Machine Learning - Nextgov

Highlights in the use of artificial intelligence in 2021

The year 2021 was profoundly challenging for citizens, companies, and governments around the world. As covid-19 spread, requiring far-reaching health and safety restrictions, artificial intelligence (AI) applications played a crucial role in saving lives and fostering economic resilience. Research and development (R&D) to enhance core AI capabilities, from autonomous driving and natural language processing to quantum computing, continued unabated.

It typically takes years, if not decades, to develop a new vaccine. But by March 2020, vaccine candidates to fight covid-19 were already undergoing human tests, just three months after the first reported cases. The record speed of vaccine development was partly thanks to AI models that helped researchers analyze vast amounts of data about coronavirus.

There are tens of thousands of subcomponents to the outer proteins of a virus. Machine learning models can sort through this blizzard of data and predict which subcomponents are the most immunogenici.e., capable of producing an immune responseand thereby guide researchers in designing targeted vaccines. The use of AI in vaccine development may revolutionize the way all vaccines are created in the future.

Autonomous driving technology continued to mature in 2021, with the industrys leading companies testing driverless cars and opening up robotaxi services to the public in various cities. Fully automated driving, which enables rides without a human safety driver on board, will be necessary for the scalability and commercialization of autonomous driving.

In 2021, natural language systems became significantly more advanced at processing aspects of human language like sentiment and intent, generating language that aligns with human speaking and writing patterns, and even visual understanding, meaning the capability to express understanding about an image through language. These natural language models are powering more accurate search results and more sophisticated chatbots and virtual assistants, leading to better user experiences and creating value for businesses. Visual understanding lays the foundation for computer systems to physically interact in everyday scenes, as it involves both understanding visual content and expressing it through language. It will be crucial for improving the quality of human-machine interaction.

Quantum computing made significant inroads in 2021, including the Jiuzhang computers achievement of quantum supremacy. This carries significance for AI, since quantum computing has the potential to supercharge AI applications compared to binary-based classical computers. For example, quantum computing could be used to run a generative machine learning model through a larger dataset than a classical computer can process, thus making the model more accurate and useful in real-world settings. Advanced technologies such as deep learning algorithms are also playing an increasingly critical role in the development of quantum computing research.

AI hardware continued to develop in 2020, with the launch of several AI chips customized for specialized tasks. While an ordinary processor is capable of supporting AI tasks, AI-specific processors are modified with particular systems that can optimize performance for tasks like deep learning. As AI applications become more widespread, any increase in performance or reduction in cost can unlock more value for companies that operate a wide network of data centers for commercial cloud services, and can facilitate the companys internal operations.

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Top Remarkable Artificial Intelligence Developments that Happened in 2021 - Analytics Insight

Edge Artificial Intelligence Market Research Report by Processor (ASIC, CPU, and GPU), by Component (Services and Solution), by Source, by End-Use, by Application, by Region (Americas, Asia-Pacific, and Europe, Middle East & Africa) - Global Forecast to 2026 - Cumulative Impact of COVID-19

New York, Dec. 06, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Edge Artificial Intelligence Market Research Report by Processor, by Component, by Source, by End-Use, by Application, by Region - Global Forecast to 2026 - Cumulative Impact of COVID-19" - https://www.reportlinker.com/p06168877/?utm_source=GNW

The Global Edge Artificial Intelligence Market size was estimated at USD 572.00 million in 2020 and expected to reach USD 701.73 million in 2021, at a CAGR 23.35% to reach USD 2,014.99 million by 2026.

Market Statistics:The report provides market sizing and forecast across five major currencies - USD, EUR GBP, JPY, and AUD. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2019 are considered historical years, 2020 as the base year, 2021 as the estimated year, and years from 2022 to 2026 are considered the forecast period.

Market Segmentation & Coverage:This research report categorizes the Edge Artificial Intelligence to forecast the revenues and analyze the trends in each of the following sub-markets:

Based on Processor, the market was studied across ASIC, CPU, and GPU.

Based on Component, the market was studied across Services and Solution. The Services is further studied across Support & Maintenance, System Integration & Testing, and Training & Consulting. The Solution is further studied across Platform and Software Tools.

Based on Source, the market was studied across Biometric Data, Mobile Data, Sensor Data, Speech Recognition, and Video & Image Recognition.

Based on End-Use, the market was studied across Automotive, Energy and Utilities, Government & Public Sector, Healthcare, Manufacturing, and Telecom.

Based on Application, the market was studied across Access Management, Autonomous Vehicles, Energy Management, Precision Agriculture, Remote Monitoring & Predictive Maintenance, Smart Wearables, Telemetry, and Video Surveillance.

Based on Region, the market was studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand. The Europe, Middle East & Africa is further studied across France, Germany, Italy, Netherlands, Qatar, Russia, Saudi Arabia, South Africa, Spain, United Arab Emirates, and United Kingdom.

Cumulative Impact of COVID-19:COVID-19 is an incomparable global public health emergency that has affected almost every industry, and the long-term effects are projected to impact the industry growth during the forecast period. Our ongoing research amplifies our research framework to ensure the inclusion of underlying COVID-19 issues and potential paths forward. The report delivers insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The updated study provides insights, analysis, estimations, and forecasts, considering the COVID-19 impact on the market.

Competitive Strategic Window:The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:The FPNV Positioning Matrix evaluates and categorizes the vendors in the Edge Artificial Intelligence Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

Competitive Scenario:The Competitive Scenario provides an outlook analysis of the various business growth strategies adopted by the vendors. The news covered in this section deliver valuable thoughts at the different stage while keeping up-to-date with the business and engage stakeholders in the economic debate. The competitive scenario represents press releases or news of the companies categorized into Merger & Acquisition, Agreement, Collaboration, & Partnership, New Product Launch & Enhancement, Investment & Funding, and Award, Recognition, & Expansion. All the news collected help vendor to understand the gaps in the marketplace and competitors strength and weakness thereby, providing insights to enhance product and service.

Company Usability Profiles:The report profoundly explores the recent significant developments by the leading vendors and innovation profiles in the Global Edge Artificial Intelligence Market, including Adlink Technology, Inc., Amazon Web Services Inc., Anagog Ltd., Cato Networks, Ltd., ClearBlade, Inc., Cloudera, Inc., Edge Intelligence Software, Inc., EdgeConneX, EdgeIQ, Eta Compute Inc., FogHorn Systems, Google LLC by Alphabet Inc., Gorilla Technology Inc., Hewlett Packard Enterprise Company, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, Nutanix, Inc., Octonion SA, Saguna, Synaptics Incorporated, Tata Elxsi Limited, TIBCO Software Inc., Vapor IO, and Vector ITC.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on the market offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:1. What is the market size and forecast of the Global Edge Artificial Intelligence Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Edge Artificial Intelligence Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Edge Artificial Intelligence Market?4. What is the competitive strategic window for opportunities in the Global Edge Artificial Intelligence Market?5. What are the technology trends and regulatory frameworks in the Global Edge Artificial Intelligence Market?6. What is the market share of the leading vendors in the Global Edge Artificial Intelligence Market?7. What modes and strategic moves are considered suitable for entering the Global Edge Artificial Intelligence Market?Read the full report: https://www.reportlinker.com/p06168877/?utm_source=GNW

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